What you’ll do

As a Staff Software Engineer on the Identity & Access Management (IAM) team, you will be a primary architect of CoreWeave’s IAM strategy as we rapidly expand the platform. You will leverage your deep expertise in identity systems to design, extend, and harden a platform that secures our high-performance cloud infrastructure. This is a high-impact leadership role where you will balance rapid execution with the long-term technical vision necessary to meet the demands of our global enterprise customers.

About the role

• Technical Strategy & Vision: Partner with Product and Engineering Leadership to translate complex requirements into a multi-year technical roadmap that balances immediate business needs with long-term architectural integrity
• System Architecture: Lead the design and implementation of high-throughput, low-latency distributed systems for authentication and authorization across all CoreWeave services
• Platform Evolution: Architect customer-facing experiences for identity lifecycle management, IdP integration (SAML/OIDC), and granular permissioning models
• Engineering Excellence: Champion a security-first mindset across the organization, establishing rigorous standards for code quality, design reviews, and threat modeling
• Operational Leadership: Elevate the team’s operational maturity by defining SLOs, enhancing deep-stack observability, and driving automation to eliminate systemic toil
• Technical Mentorship: Act as a force multiplier by mentoring Senior engineers and fostering a culture of continuous learning and high-performance engineering
• Cross-Functional Influence: Drive alignment on critical security initiatives across the broader engineering organization, articulating complex technical trade-offs to stakeholders

Who you are

• 10+ Years of Experience building and operating large-scale distributed systems in a production environment using one or more of Go, TypeScript, or React
• 5+ Years of IAM Domain Expertise: Proven track record of developing identity products and a deep mastery of standards including OIDC, OAuth2.0, SAML, and JWT
• Technical Leadership: Demonstrated experience leading large, cross-functional projects from inception to planet-scale deployment with measurable business impact
• Cloud-Native Mastery: Deep understanding of Kubernetes, microservices architecture at scale, and advanced CI/CD patterns
• Enterprise Security Acumen: Solid knowledge of the security, compliance (SOC2, HIPAA, etc.), and governance needs of sophisticated enterprise customers
• Strategic Communication: Superb ability to synthesize ambiguous requirements into clear technical designs and communicate trade-offs to both executive and technical audiences
• Analytical Problem Solving: Expert-level ability to debug complex, intermittent issues within distributed environments

Preferred

• Architectural Transformation: Experience leading the decomposition of monolithic architectures into high-performance microservices
• AI-Augmented Development: Familiarity with leveraging AI agents (e.g., Claude, Cursor) to accelerate the software development lifecycle
• Emerging Tech Knowledge: Familiarity with AI/ML workloads, GPU-based infrastructure, and the unique security challenges of MLOps at scale
• Policy-as-Code: Experience with modern authorization frameworks such as Open Policy Agent (OPA) or Cedar

Wondering if you’re a good fit? We believe in investing in our people, and value candidates who can bring their own diversified experiences to our teams – even if you aren't a 100% skill or experience match. Here are a few qualities we’ve found compatible with our team. If some of this describes you, we’d love to talk. 

• You love to solve complex problems and build high-scale, low-latency distributed systems.
• You’re curious about how to evolve cloud platforms and scale infrastructure globally for the next generation of AI workloads.
• You’re an expert in one or more preferred programming languages (Go, TypeScript, Python), and have deep technical knowledge of distributed systems.

Why CoreWeave?

At CoreWeave, we work hard, have fun, and move fast!  We’re in an exciting stage of hyper-growth that you will not want to miss out on. We’re not afraid of a little chaos, and we’re constantly learning. Our team cares deeply about how we build our product and how we work together, which is represented through our core values: 

• Be Curious at Your Core
• Act Like an Owner
• Empower Employees
• Deliver Best-in-Class Client Experiences
• Achieve More Together

We support and encourage an entrepreneurial outlook and independent thinking. We foster an environment that encourages collaboration and enables the development of innovative solutions to complex problems. As we get set for takeoff, the organization's growth opportunities are constantly expanding. You will be surrounded by some of the best talent in the industry, who will want to learn from you, too. Come join us!

The base salary range for this role is $188,000 to $275,000. The starting salary will be determined based on job-related knowledge, skills, experience, and market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility). 

Come Work With Us:

Metropolitan Commercial Bank (“MCB” or the “Bank”) is a New York City–based, full-service commercial bank providing tailored banking solutions to businesses, institutions, and individuals. Founded in 1999, MCB operates banking centers in Manhattan and Boro Park, Brooklyn, within New York City, as well as in Great Neck on Long Island, New York, and Lakewood, New Jersey. The Bank recently expanded to Miami, Florida with their newest Brickell banking center.

Metropolitan Commercial Bank offers a comprehensive suite of commercial, business, and personal banking products and services to small businesses, middle-market and corporate enterprises, private and public institutions, municipalities, and local government entities.

The Bank has earned national recognition for its financial performance, innovation, and strategic growth. The Bank was named one of Newsweek’s Best Regional Banks in 2024 and 2025. Additionally, MCB recently received Editor’s Choice recognition at the Banking Tech Awards USA for Digital Onboarding & Omnichannel Banking and in 2026, the Bank earned Great Place To Work certification and received the Web Award Standard of Excellence for MCBankNY.com.

We are a client-focused organization that values technological innovation and excellence. A strong technical mindset, AI fluency, and adaptive skills are essential for our employees to effectively contribute to our mission and drive our success. We foster human–AI teaming and strong governance to ensure technology is used responsibly and in alignment with Bank policies and procedures. For more information about the Bank, please visit the Bank’s website at MCBankNY.com.

Position Summary:

Metropolitan Commercial Bank is seeking a VP-level Applied AI & Machine Learning Scientist to design, build, and validate production-grade AI/ML and Generative AI solutions in a highly regulated banking environment. This role focuses on high-impact use cases—fraud detection, AML alert optimization, AI-assisted credit memo generation for underwriting decision support, contact center AI assistant/copilots, and personalization for treasury/commercial clients—delivered with rigorous governance, explainability, fairness testing, privacy-by-design, cybersecurity, and model lifecycle controls aligned to SR 11-7 and MCB’s Trustworthy & Responsible AI Principles. The role emphasizes Snowflake as the primary ML platform (e.g., Snowpark Python, UDFs/UDTFs, Tasks/Streams, and Snowflake-native ML).

Standard 4-day in-office requirement, 1 day remote (of your choosing)

Essential Functions & Responsibilities 

Applied AI/ML development:

• Design and implement models for fraud detection, AML alert scoring/triage, AI-generated credit memo drafting and underwriting decision support, contact center AI assistants, and personalization for commercial/treasury use cases.
• Leverage modern methods: Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), embeddings and vector databases, transformers, boosting, anomaly/outlier detection, and classical ML.
• Embed explainability (e.g., SHAP, interpretable scorecards/monotonic models) and conduct pre-/post-deployment bias testing with documented remediation.

Model validation, documentation & governance (SR 11‑7):

• Produce audit-ready documentation (methodology, assumptions, data lineage, limitations, testing) and register models in the inventory with owners/materiality.
• Facilitate independent validation/effective challenge; obtain required approvals before deployment; maintain change management and periodic review cadence.
• Define monitoring, drift thresholds, retraining triggers, and safe rollback/kill-switch procedures; maintain human-in-the-loop checkpoints for high-impact decisions.

Productionization & MLOps on Snowflake

• Package, deploy, and operate models via CI/CD, containerization, and model registry; instrument KPIs/KRIs and alerting dashboards. Operate models natively on Snowflake using Snowpark Python, UDFs/UDTFs, Tasks/Streams, and secure external access where required.
• Partner with Engineering to integrate models via secure APIs/batch; ensure scalability, resiliency, and observability in cloud/on‑prem (e.g., Snowflake, Azure ML, Databricks).

Regulatory, privacy, and cybersecurity alignment:

• Design for ECOA/Reg B (adverse action specificity), UDAAP, FCRA, GLBA privacy, and NYDFS 23 NYCRR 500 cybersecurity requirements.
• Apply privacy-by-design (data minimization, purpose limitation, retention), strong access controls/segregation, and secure SDLC/red teaming for GenAI stacks.

Third‑party AI & data stewardship:

• Support due diligence, testing, and ongoing monitoring of vendor AI/data providers per SR 23‑4; evaluate conceptual soundness, fairness, and security.
• Negotiate/verify contractual controls (no vendor training on MCB/NPI, subprocessors disclosure, audit rights, exit/portability).
• Ensure AEDT compliance (NYC Local Law 144) for any HR-related AI tools.

Cross‑functional partnership:

• Collaborate with Model Risk, Compliance/Legal, Cyber/IT, Data Privacy, Internal Audit, and business owners to meet objectives while staying within risk appetite.
• Communicate complex results, risks, and limitations clearly to technical and non‑technical stakeholders (management committees, examiners).

Innovation, coaching, and best practices:

• Evaluate emerging ML/GenAI methods, LLM evaluation techniques, Snowflake‑native capabilities (e.g., vector search, orchestration), and governance tooling; lead POCs within established control gates.
• Mentor junior staff; promote responsible AI practices, documentation standards, and reproducibility.

Qualifications & Skills:

• 6+ years of relevant work experience.
• Expertise in Python (pandas, scikit‑learn), deep learning (PyTorch/TensorFlow), NLP/LLMs, LangChain, embeddings/vector search, and classic ML.
• MLOps proficiency with CI/CD, containerization (Docker), registries, and observability; cloud ML (Snowflakes-native ML, Azure ML or Databricks preferred).
• Snowflake‑native ML proficiency: Snowpark Python, UDFs/UDTFs, Tasks/Streams; ability to build and operate ML workflows inside Snowflake.
• Data engineering competency (SQL, ETL/pipelines, Spark/PySpark); ability to work with structured/unstructured data.
• Explainability (e.g., SHAP) and fairness testing; ability to produce interpretable reason codes for ECOA/Reg B adverse actions as applicable.
• Strong grasp of SR 11‑7 lifecycle, model documentation, and operational monitoring within three lines of defense governance.
• Excellent communication; ability to translate technical detail to business/risk stakeholders and drive decisions.
• Curiosity and problem‑solving mindset; ability to balance innovation with disciplined risk management.

Preferred Qualifications & Skills

• Master’s or PhD in a relevant field (Computer Science, Machine Learning, Data Science, Statistics, etc.) is strongly preferred, especially with research or thesis work related to AI/ML, NLP, or model interpretability.
• Financial services domain experience (fraud risk, AML, underwriting, or commercial/treasury analytics).
• Hands-on with Snowflake ML/Snowpark (Python), Tasks/Streams, secure external functions; experience with feature management/registry tooling a plus. model registry and pipeline orchestration; Kubernetes a plus.
• RAG architectures, vector databases, prompt engineering, and LLM evaluation (accuracy, hallucination, safety).
• Fairness toolkits and XAI frameworks; experience preparing models for validation, audit, or regulatory exam discussions.
• Familiarity with SR 23‑4 (third‑party risk), NYC Local Law 144 (AEDT), NYDFS Part 500 (cyber).
• Ability to work in a constantly evolving environment
• Must have excellent written and verbal communication skills
• Must be a good listener and good teacher
• Demonstrate analytical, troubleshooting and problem-solving skills
• The ability to learn new technologies quickly
• Self-directed individual with technology and communication skills.
• Ability to take in multiple sources of information with an understanding of the bigger picture need, want, and operation of the Bank.
• Collaborative team-player who can find creative and practical solutions in a dynamic work environment.
• Ability to handle ambiguity, juggle multiple matters at once, and quickly and seamlessly shift from one situation or task to another.

Potential Salary: $130,000 - $200,000 annually

This salary range reflects base wages and does not include benefits, bonus, or incentive pay. Salary bands are purposefully wide ranging to encompass the different factors considered in determining where a candidate falls in the range, including but not limited to, seniority, performance, experience, education, and any other legitimate, non-discriminatory factor permitted by law. Final offer amounts are determined by multiple factors including candidate experience and expertise and may vary from the amounts listed here.

Metropolitan Commercial Bank provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws.

This applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.

Come Work With Us:

Metropolitan Commercial Bank (“MCB” or the “Bank”) is a New York City–based, full-service commercial bank providing tailored banking solutions to businesses, institutions, and individuals. Founded in 1999, MCB operates banking centers in Manhattan and Boro Park, Brooklyn, within New York City, as well as in Great Neck on Long Island, New York, and Lakewood, New Jersey. The Bank recently expanded to Miami, Florida with their newest Brickell banking center.

Metropolitan Commercial Bank offers a comprehensive suite of commercial, business, and personal banking products and services to small businesses, middle-market and corporate enterprises, private and public institutions, municipalities, and local government entities.

The Bank has earned national recognition for its financial performance, innovation, and strategic growth. The Bank was named one of Newsweek’s Best Regional Banks in 2024 and 2025. Additionally, MCB recently received Editor’s Choice recognition at the Banking Tech Awards USA for Digital Onboarding & Omnichannel Banking and in 2026, the Bank earned Great Place To Work certification and received the Web Award Standard of Excellence for MCBankNY.com.

We are a client-focused organization that values technological innovation and excellence. A strong technical mindset, AI fluency, and adaptive skills are essential for our employees to effectively contribute to our mission and drive our success. We foster human–AI teaming and strong governance to ensure technology is used responsibly and in alignment with Bank policies and procedures. For more information about the Bank, please visit the Bank’s website at MCBankNY.com.

Position Summary:

Metropolitan Commercial Bank (the “Bank”) is seeking a VP-level AI/ML Engineer to deploy AI solutions at enterprise scale, with a strong emphasis on Large Language Model (LLM) applications and modern MLOps & AIOps practices. This role sits at the intersection of data science and software engineering, reporting to the manager of the IT Application Development and Support team and working closely with the Chief Artificial Intelligence Officer, transforming innovative AI prototypes into robust, scalable production systems. The AI/ML Engineer will lead the deployment of high-impact AI capabilities (e.g., generative AI systems, personalization engines, automation tools) and ensure scalable AI platforms that deliver real-world value. The role also includes designing, constructing, and maintaining the Bank’s AIOps solution, with Snowflake as the primary ML platform (e.g., Snowpark Python, UDFs/UDTFs, Tasks/Streams, and Snowflake-native ML).

Standard 4-day in-office requirement, 1 day remote (of your choosing)

Essential Functions & Responsibilities 

Production Architecture & AIOps

• Establish and enforce architecture standards for production AI systems, including data pipelines, model serving infrastructure, and real-time inference services.
• Implement AIOps/MLOps pipelines for CI/CD of ML models, model governance, monitoring, and lifecycle management.
• Design and maintain scalable software applications with integrated AI/ML capabilities.
• Develop software architecture and design patterns to ensure performance and scalability.
• Write clean, maintainable code in general-purpose programming languages (Python, Java, C, C++, Go).
• Implement and manage data pipelines for preprocessing and transforming data for AI/ML models.
• Integrate AI/ML models into production environments and optimize for reliability and scalability.
• Apply Site Reliability Engineering (SRE) principles and implement monitoring and alerting solutions.
• Conduct code reviews and provide technical guidance to junior developers.
• Stay current with advancements in software engineering and AI/ML technologies.
• Adhere to agile and lean software development best practices.
• Thoroughly document all developed models and processes according to relevant policies and standards.
• Support the production environment by either resolving technical or functional issues, in line with the procedures defined by the Bank.

Cross-Functional Collaboration

• Partner with data scientists, AI scientists, product managers, data engineers, DevOps, and business stakeholders to operationalize AI algorithms.
• Mentor or train teams and coordinate between research-oriented AI scientists and engineering teams to continuously improve models with production feedback.

Scaling & Performance

• Ensure AI solutions perform at scale, handling thousands of daily inferences with low latency and high reliability.
• Optimize model serving using techniques like model compression, caching, and hardware acceleration.

Implement robust monitoring and alerting for model performance to detect and address degradation (e.g., drift, latency issues).

Qualifications & Skills:

• 6+ years of experience
• Master’s or PhD in a relevant field (Computer Science, Software Engineering,  Machine Learning, Data Science, Statistics, etc.) is strongly preferred, especially with research or thesis work related to AI/ML
• LLM & GenAI Mastery: Expert in building and deploying LLM-based applications using RAG, prompt engineering, and vector databases. Skilled in LLMOps tools (LangChain, LlamaIndex) and fine-tuning models for enterprise use, including agent-based architectures.
• MLOps & Cloud Infrastructure: Proficient in cloud ML platforms (AWS, GCP, Azure) and MLOps workflows. Uses Docker, Kubernetes, and IaC tools (Terraform, CloudFormation) for scalable deployments. Experienced in CI/CD, real-time inference, GPU optimization, and ML observability (Prometheus, Grafana, MLflow).
• Full-Stack Development: Capable of building end-to-end AI solutions, from front-end (React) to back-end APIs (Flask, FastAPI, Node.js). Skilled in integrating ML models with databases (SQL, NoSQL) and delivering seamless user experiences through robust software engineering.
• Proficient in Python (pandas, scikit-learn), deep learning (PyTorch/TensorFlow/Keras), NLP/LLMs, LangChain, embeddings/vector search, and classic ML.
• Experienced with Snowflake-native ML (Snowpark Python, UDFs/UDTFs, Tasks/Streams).
• Competent in data engineering (SQL, ETL/pipelines, Spark/PySpark) and handling large structured/unstructured datasets.
• Strong understanding of AI/ML algorithms, application architecture, and design patterns.
• Excellent problem-solving, analytical, communication, and collaboration skills.

Preferred Qualifications & Skills

• Financial services domain experience (fraud risk, AML, underwriting, or commercial/treasury analytics).
• Hands-on experience with Snowflake ML/Snowpark (Python), Tasks/Streams, secure external functions, and feature management/registry tooling.
• Familiarity with fairness toolkits, XAI frameworks, and preparing models for validation, audit, or regulatory exams.
• Knowledge of SR 23‑4 (third‑party risk), NYC Local Law 144 (AEDT), NYDFS Part 500 (cyber).
• Ability to work in a constantly evolving environment.
• Excellent written and verbal communication skills.
• Strong listening and teaching abilities.
• Demonstrated analytical, troubleshooting, and problem-solving skills.
• Quick learner of new technologies.
• Self-directed with strong technology and communication skills.
• Ability to synthesize multiple sources of information and understand the broader operational context of the Bank.
• Collaborative team player with a practical and creative approach in a dynamic work environment.
• Ability to handle ambiguity, multitask, and adapt quickly to changing priorities.

Potential Salary: $130,000 - $200,000 annually

This salary range reflects base wages and does not include benefits, bonus, or incentive pay. Salary bands are purposefully wide ranging to encompass the different factors considered in determining where a candidate falls in the range, including but not limited to, seniority, performance, experience, education, and any other legitimate, non-discriminatory factor permitted by law. Final offer amounts are determined by multiple factors including candidate experience and expertise and may vary from the amounts listed here.

Metropolitan Commercial Bank provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws.

This applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.

The team 

Nebius is building a world-class commercial organization to match the scale of our infrastructure platform. Our GTM leadership team operates with high trust, direct access to Product and Engineering, and the mandate to define how Nebius competes and wins in the enterprise. You will join a senior leadership bench that values clarity, accountability, and speed — with fewer layers and more impact than the hyperscalers. 

The role 

Nebius is seeking an Enterprise Sales Leader to build and scale our industry go-to-market across priority verticals including Financial Services, Healthcare & Life Sciences, Manufacturing, Media & Entertainment, Automotive, and Energy. 

This leader will: 

• Build a verticalized enterprise sales motion 

• Lead a team of Enterprise AEs aligned to industry segments 

• Drive repeatable, multi-year AI infrastructure deals 

• Operationalize a partner-led GTM model anchored in NVIDIA and strategic SIs 

Success in this role requires combining vertical expertise, ecosystem orchestration, and enterprise deal execution to establish Nebius as the AI infrastructure platform of choice. 

Success will be measured not only by direct revenue, but by partner-influenced pipeline, multi-year capacity commitments, and lighthouse customer acquisition. 

You are welcome to work remotely from the US. 

Partner & NVIDIA Motion 

This role will lead a partner-centric enterprise GTM model: 

• Own joint go-to-market with NVIDIA field teams across priority verticals 

• Build and scale co-sell motions with SIs and channel partners (Accenture, Deloitte, Slalom, CDW, WWT, etc.) 

• Enable ISVs to embed Nebius into enterprise workflows and solutions 

• Drive partner-sourced and partner-influenced pipeline as a core KPI 

• Establish joint value propositions combining infrastructure + software + services 

Your responsibilities will include: 

• Define and execute vertical-specific GTM strategies aligned to NVIDIA’s top enterprise markets. 

• Hire, develop, and lead a team of Enterprise Account Executives focused on industry specialization — setting performance standards, coaching deal execution, and driving vertical expertise. 

• Own revenue targets and ARR growth across assigned verticals. 

• Develop named account strategies within priority enterprise segments. 

• Personally engage in complex, multi-million-dollar GPU infrastructure deals — connecting infrastructure decisions to vertical-specific business outcomes. 

• Build executive-level relationships across CIO, CTO, Chief Data, and Line-of-Business leaders. 

• Collaborate closely with NVIDIA field teams to drive joint account planning and pipeline development. 

• Establish forecasting rigor, pipeline discipline, and enterprise sales standards across the vertical portfolio. 

• Partner cross-functionally with CSA, Product, Marketing, Capacity Planning, and Partnerships to align resources and deliver differentiated value. 

• Drive land-and-expand motions across AI training, inference, and production AI workloads. 

• Evangelize Nebius’s NVIDIA-based GPU platform (H100, H200, B200, GB200 NVL72, and beyond) within vertical-specific use cases. 

• Build vertical-specific solution plays (RAG, training, inference, MLOps) with partners 

• Drive partner-led access to enterprise buying centers (CIO, CDAO, CISO, ML teams) 

• Scale 1:few and 1:many partner GTM programs (not just direct selling) 

• Establish lighthouse customers per vertical 

We expect you to have: 

• 10+ years of enterprise infrastructure or cloud sales experience, with a proven track record in one or more large enterprise verticals. 

• 5+ years of leadership experience managing enterprise sales teams with vertical or industry specialization. 

• Proven success closing $5M–$50M+ infrastructure or platform deals within regulated or complex industry segments. 

• Strong understanding of vertical-specific AI use cases, regulatory environments, and enterprise buying cycles. 

• Experience co-selling with strategic ecosystem partners (e.g., NVIDIA, OEMs, SIs) and navigating partner-driven motions. 

• Builder mindset — you’ve created vertical-focused GTM playbooks, coverage models, and scalable sales motions where none existed. 

• Demonstrated ability to build and scale vertical-focused sales teams within high-growth companies. 

• High energy, ownership mentality, and consistent overachievement against targets. 

• Experience co-selling with NVIDIA and/or large SIs in enterprise environments 

• Proven ability to drive partner-influenced enterprise revenue at scale 

• Deep understanding of enterprise procurement + partner-driven buying cycles 

It will be an added bonus if you have: 

• Direct experience working with NVIDIA field teams or ecosystem partners on enterprise sales motions. 

• Experience selling AI/ML infrastructure into regulated or highly complex industries (e.g., FSI, Healthcare, Energy). 

• Background in hyperscaler, neo-cloud, or GPU-first environments. 

• Established executive network within one or more priority enterprise verticals. 

• Experience structuring multi-year infrastructure and capacity commitments. 

• Prior leadership at a hyperscaler or neo-cloud (e.g., AWS, Azure, GCP, CoreWeave, Lambda). 

Key Employee Benefits 

• Health Insurance: 100% company-paid medical, dental, and vision coverage for employees and families. 

• 401(k) Plan: Up to 4% company match with immediate vesting. 

• Parental Leave: 20 weeks paid for primary caregivers, 12 weeks for secondary caregivers. 

• Remote Work Reimbursement: Up to $85/month for mobile and internet. 

• Disability & Life Insurance: Company-paid short-term, long-term, and life insurance coverage. 

Compensation 

We offer competitive salaries in the 458K - 550K range + equity

Don’t just work in tech. Shape it. If you’re driven to define how the world builds with AI, we want to hear from you. 

The Role

We're looking for a Staff Backend Engineer to join our Customer Value Optimization (CVO) tribe at HelloFresh. This is a high-impact individual contributor role sitting at the intersection of Data Science and Backend Engineering — you'll be the technical bridge that turns ML models into reliable, scalable production systems.

You'll work across squads within the CVO tribe, partnering closely with Data Scientists and Backend Engineers to define how we serve ML models at scale, raise the technical bar, and accelerate the tribe's ability to deliver value to our customers.

What you’ll do

• Design, build, and own robust, scalable infrastructure for live serving of ML models in production
• Act as the technical bridge between Data Science and Backend Engineering teams within the CVO tribe — translating DS needs into scalable engineering solutions
• Define and drive best practices for model serving, APIs, and system design across squads
• Lead cross-squad technical initiatives, from architecture decisions to hands-on implementation
• Partner with Data Scientists to ensure models are production-ready — performant, observable, and maintainable
• Contribute to tribe-wide engineering standards and shape the technical roadmap
• Mentor and support engineers across squads without direct line management responsibility

What you’ll bring

• 7+ years of backend engineering experience, with a strong foundation in Python
• Proven experience building and operating ML model serving systems or ML platforms in production
• Deep understanding of distributed systems, APIs, and microservices architecture
• Strong track record of working across team boundaries to deliver complex, cross-functional projects
• Excellent communication skills — able to work fluently with Data Scientists, Machine Learning Engineers, and Data Engineers
• A collaborative, low-ego approach with strong technical judgment

Nice to Have

• Experience with ML frameworks and serving tools
• Familiarity with feature stores, model registries, or MLOps tooling
• Background in e-commerce, subscription, or personalization domains

P-226

As a Specialist Solutions Architect (SSA) - AI/ML Engineering, you will be the trusted technical ML & AI expert to both Databricks customers and the Field Engineering organization. You will work with Solution Architects to guide customers in architecting production-grade ML & AI applications on Databricks, while aligning their technical roadmap with the continually evolving Databricks Data Intelligence Platform. You will continue to strengthen your technical skills through applying cutting-edge technologies in GenAI, MLOps, and ML more broadly, expanding your impact through mentorship, and establishing yourself as an AI thought leader.

The impact you will have:

• Architect production-level ML & AI workloads for customers using our unified platform, including agents, end-to-end ML pipelines, training/inference optimization, integration with cloud-native services, MLOps, etc.  
• Serve as a trusted practitioner for enterprise GenAI solutions, including RAG architectures, agentic systems (tool-calling agents, multi-agent orchestration, guardrails), natural language querying of structured data, AI evaluation and observability, and monitoring systems
• Build, scale, and optimize customer AI workloads and apply best-in-class MLOps to productionize these workloads across a variety of domains
• Provide advanced technical support to Solution Architects during the technical sale, ranging from feature engineering, training, tracking, serving, to model monitoring, all within a single platform, as well as participating in the larger ML SME community in Databricks
• Collaborate cross-functionally with the product and engineering teams to represent the voice of the customer, define priorities, and influence the product roadmap, helping with the adoption of Databricks’ AI offerings

What we look for:

• 5+ years of hands-on industry ML experience in at least one of the following:
• ML Engineer: Build and maintain production-grade cloud (AWS/Azure/GCP) infrastructure that supports the deployment of ML applications, including drift monitoring.
• AI Engineer: Experience with the latest techniques in LLMs & agentic systems, including vector databases, fine-tuning LLMs, AI guardrail systems, and deploying LLMs with tools such as HuggingFace, Langchain, and OpenAI
• Graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics, Operations Research, etc.) or equivalent practical experience
• Experience communicating and/or teaching technical concepts to non-technical and technical audiences alike
• Passion for collaboration, life-long learning, and driving business value through ML & AI
• [Preferred] 2+ years customer-facing experience in a pre-sales or post-sales role
• Can meet expectations for technical training and role-specific outcomes within 3 months of hire
• This role can be remote, but we prefer that you be located in the job listing area and can travel up to 30% when needed

Biohub operates one of the largest AI compute clusters dedicated to biology, spanning three frontier research institutes with some of the world's leading biologists. We're not a startup trying to find product-market fit, and we're not a pharma company optimizing a pipeline. We're building frontier AI for fundamental science, as open science, at a scale no one else is doing. This is a unique moment for scientific acceleration. The problems are among the hardest and most impactful problems you can choose to work on, and we move at a pace that meets this moment. 

Our research spans:

• Frontier molecular modeling, from protein language models (e.g., ESM) to structure prediction (e.g., ESMFold) and beyond. 
• Scaled biological foundation models trained on some of the largest GPU clusters dedicated to science
• Imaging foundation models trained across the world's largest microscopy datasets
• Reasoning and agentic systems that connect frontier LLMs with biological foundation models
• Mechanistic interpretability of biological foundation models: extracting new biological knowledge directly from model weights
• Scientific data at unprecedented scale: AI systems to collect, curate, and learn from some of the richest biological datasets ever assembled

Join our Team!

As an ML Engineer, you'll join some of the strongest infrastructure engineers in AI, building the systems that connect everything together. The infrastructure problems you solve directly determine what science becomes possible. 

What You'll Do

• Work with high-dimensional scientific data formats and contribute to backend compatibility, format evaluation, and I/O performance benchmarking at petabyte scale.
• Define and shape the engineering patterns your team and collaborating researchers will build on for years; the abstractions you write today become the foundation others depend on at scale.
• Work at the intersection of AI systems and biological discovery, where the infrastructure problems you solve directly determine what science becomes possible.
• Deploy models to production and manage artifact tracking across models and datasets.
• Design and optimize GPU-native data loading pipelines for large-scale multi-dimensional tensor workloads, including profiling and resolving hardware utilization bottlenecks across multi-backend systems.
• Work on simplification and improvement of codebase abstractions to accelerate research momentum.
• Build and maintain primitives for pre-training infrastructure that ensure the reliability and continuity of large-scale training runs.
• Help cultivate best practices in MLOps, and think about the full ML lifecycle, including data, fine-tuning, deployment, reliability and monitoring.
• Possesses the ability to execute complex modifications to the research pipeline, such as fast data loading and distributed training.
• Handle DevOps responsibilities, focused on making all engineers and researchers more productive. This includes tasks like cluster monitoring, unit testing and integration testing of research codebase, and continuous integration.
• Collaborate with partner researchers and engineers to deploy our technology within external infrastructure.

What You'll Bring

• Hands-on experience with PyTorch, including custom training loops, distributed training, or low-level performance work.
• Familiarity with GPU-native data I/O tools and large-scale tensor formats (e.g. Zarr, HDF5, TensorStore, or similar).
• Experience with distributed computing frameworks such as Apache Spark, Dask, or Ray.
• Familiarity with containerization and orchestration tools such as Docker and Kubernetes.
• Experience building or working with AI agent frameworks is a plus.
• A track record of building systems that other engineers and researchers depend on. Not just running experiments, but shipping infrastructure that scales.

Compensation

The future anticipated Redwood City, CA, and New York City, NY base pay range for a role in this field is $150,000 to $350,000+ annually. Compensation ranges will vary based on job-related skills, level of experience, and knowledge. Actual placement in range is based on job-related skills and experience, as evaluated throughout the interview process.

Benefits for the Whole You 

We’re thankful to have an incredible team behind our work. To honor their commitment, we offer a wide range of benefits to support the people who make all we do possible. 

• Provides a generous employer match on employee 401(k) contributions to support planning for the future.
• Paid time off to volunteer at an organization of your choice. 
• Funding for select family-forming benefits. 
• Relocation support for employees who need assistance moving

Please note that applying to this opportunity does not guarantee that we will be in touch with you regarding our opportunities. Our recruiting team will contact you if your experience aligns with the skills we seek for future open positions. We will keep your interest on file, contact you as opportunities arise, and send you information about the exciting work we are doing at Biohub. You can opt out at any time! 

#LI-Hybrid

About the Role

The Platform Infrastructure team at iCapital plays a critical role in ensuring that both production and development environments operate smoothly, securely, and reliably. This role leverages advanced cloud capabilities to support the Platform Infrastructure strategy of market agility and lean operating principles, with a strong emphasis on quality to meet the ever‑growing demands of our clients.

We are seeking highly collaborative, creative, and intellectually curious MLOps/DevOps Engineers with deep expertise in machine learning operations, cloud infrastructure, CI/CD automation, Kubernetes, and security. This role requires hands‑on experience designing, building, and operating scalable DevOps and enterprise‑grade MLOps platforms, including model lifecycle automation, observability, and governance.

As a Platform Engineer, you will wear multiple hats in a highly visible role, partnering closely with engineering, security, data, and business teams to deliver secure, reliable, and highly automated platforms that support both application and machine‑learning workloads.

Responsibilities

• Design, build, and operate MLOps pipelines supporting the full ML lifecycle (training, validation, deployment, monitoring).
• Enable production workloads for AI/ML and Generative AI systems, including LLM‑based services.
• Develop and maintain CI/CD pipelines for AI/ML services and supporting infrastructure.
• Build and manage cloud‑native infrastructure on AWS, with heavy use of Kubernetes and containerized workloads.
• Automate infrastructure provisioning and configuration using Infrastructure as Code (Terraform).
• Implement model versioning, experiment tracking, and artifact management across environments.
• Ensure reliability, scalability, observability, and cost efficiency of AI platforms.
• Partner with AI/ML engineers to operationalize models and standardize deployment patterns.
• Implement monitoring and alerting for system health, model performance, and drift.
• Enforce security, compliance, and governance requirements for AI workloads.
• Participate in incident response, root cause analysis, and continuous improvement initiatives.
• Document standards, best practices, and reference architectures for MLOps and AI infrastructure.

Required Qualifications

• 15+ years of experience in DevOps, SRE, or Platform Engineering, with AWS as a primary cloud.
• Experience supporting machine learning systems in production, including deployment and monitoring concerns.
• Hands‑on experience with machine learning platforms, particularly AWS SageMaker (required).
• Strong hands-on experience with Kubernetes, containerized workloads, and cloud networking.
• Proven experience building and operating CI/CD pipelines (e.g., GitLab CI, ArgoCD).
• Strong proficiency with Terraform and scripting/programming in Python or similar languages.
• Solid Linux, systems, and troubleshooting fundamentals.
• Excellent communication skills and ability to work across teams.
• Direct experience with MLOps platforms and tooling (model registries, experiment tracking, feature stores).
• Exposure to Generative AI / LLM workloads in production environments.
• Familiarity with data stores commonly used in ML systems (e.g., Postgres, DynamoDB, object storage).
• Experience operating in regulated or fintech environments.
• Background in cost optimization for compute‑intensive workloads.
• Strong written and verbal communication skills.
• AWS certifications are a plus.

Benefits

The base salary range for this role is $180,000 to $230,000 depending on experience.  iCapital offers a compensation package which includes salary, equity for all full-time employees, and an annual performance bonus. Employees also receive a comprehensive benefits package that includes an employer matched retirement plan, generously subsidized healthcare with 100% employer paid dental, vision, telemedicine, and virtual mental health counseling, parental leave, and unlimited paid time off (PTO).

We believe the best ideas and innovation happen when we are together. Employees in this role will work in the office Monday-Thursday, with the flexibility to work remotely on Friday.

For additional information on iCapital, please visit https://www.icapitalnetwork.com/about-us  Twitter: @icapitalnetwork | LinkedIn: https://www.linkedin.com/company/icapital-network-inc | Awards Disclaimer: https://www.icapitalnetwork.com/about-us/recognition/

iCapital is proud to be an Equal Employment Opportunity and Affirmative Action employer. We do not discriminate based upon race, religion, color, national origin, gender, sexual orientation, gender identity, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics

Who We Are

PathAI's mission is to improve patient outcomes with AI-powered pathology. Our platform promises substantial improvements to the accuracy of diagnosis and the efficacy of treatment of diseases like cancer, leveraging modern approaches in machine learning and artificial intelligence. We have a track record of success in deploying AI algorithms for histopathology in translational research, pathology labs and clinical trials.  Rigorous science and careful analysis is critical to the success of everything we do. Our team, composed of diverse employees with a wide range of backgrounds and experiences, is passionate about solving challenging problems and making a huge impact on patient outcomes.

Where You Fit 

Our Intern/Co-op program is designed to give you a real-world view of what it's like to be a Machine Learning Engineer at PathAI. You will be embedded directly into our AI product and ML Core teams, working on active projects that have a direct impact on our technical roadmap and business goals.

You will collaborate with a cross-functional team of scientists, engineers, and product managers to translate clinical needs into scalable ML solutions. This is your chance to contribute to meaningful work that directly relates to patient outcomes.

Timeline: This opportunity is for Fall 2026. We are looking for interns/co-ops who can commit to 3-6 months at PathAI (4-6 months preferred).

Location: We have offices in Boston, MA and New York, NY and would prefer interns/co-ops who can work from one of those locations—however, we will also consider remote candidates (US based only).

What You’ll Do

• Contribute to the design, development, and evaluation of machine learning models for our AI products, AI services and core ML models.
• Work on subprojects derived from our ongoing ML product roadmap, focusing on areas like model improvements with novel techniques.
• Partner with our MLOps and platform teams to help build and improve our ML pipelines.
• Participate in experimental design, analysis, and team knowledge-sharing initiatives like design reviews, technical forums and journal clubs.

What You Bring 

• Currently pursuing a M.S., or Ph.D. in Computer Science, Electrical Engineering, Biomedical Engineering with a focus on Computer Vision and Machine Learning.
• Strong programming skills in Python and hands-on experience with ML frameworks (e.g., PyTorch, TensorFlow).
• A solid grasp of the fundamentals of machine learning and/or computer vision.
• Excellent communication skills and a desire to work in a highly collaborative, cross-functional environment.
• A passion for our mission to improve patient outcomes.
• [Nice-to-have] Any high impact contributions through any AI/ML publications, open-source projects and any AI based products.

We are looking for team players who are passionate about what they do and are willing to tackle any task to contribute to our goals. You could be exactly who we are looking for!

We Want To Hear From You

At PathAI, we are looking for individuals who are team players, are willing to do the work no matter how big or small it may be, and who are passionate about everything they do. If this sounds like you, even if you may not match the job description to a tee, we encourage you to apply. You could be exactly what we're looking for. 
PathAI is an equal opportunity employer, dedicated to creating a workplace that is free of harassment and discrimination. We base our employment decisions on business needs, job requirements, and qualifications — that's all. We do not discriminate based on race, gender, religion, health, personal beliefs, age, family or parental status, or any other status. We don't tolerate any kind of discrimination or bias, and we are looking for teammates who feel the same way.  

About the Role, Mission or Department Overview

The New AI Products and Platforms mission is a central hub for new, high-ambition AI opportunities and services at The Times. We are hiring a Senior Software Engineer to help build the backend services and infrastructure that will form our internal AI Platform.

This platform will enable teams across the company to build, deploy, and manage applications that use large language models (LLMs) to advance our journalism and our business. You will report to the Director of AI Platforms and work with a team of engineers, designers, product and project managers, data scientists, and newsroom stakeholders to make investments in AI possible across The Times.

This is a hybrid position based in our New York City headquarters. You can typically expect to come into the office two days per week.

Responsibilities:

• Design, build, and maintain scalable and reliable backend services and APIs for a central platform used by teams building AI applications across our journalism and our business.

• Take ownership of core platform components and integrations, including systems for prompt and workflow management, evaluation and tracing, and monitoring for cost, usage, and performance.

• Build and operate vector database and retrieval infrastructure to support search and AI-powered discovery experiences.

• Design and develop retrieval-augmented generation (RAG) pipelines and agentic workflows that connect our content, data, and AI models.

• Build orchestration layers for multi-step AI workflows, managing the coordination of LLM calls, retrieval, and business logic.

• Develop the infrastructure that enables teams to create and manage their AI applications, complete with metadata, observability, and access controls.

• Architect and implement data pipelines and storage solutions to support our AI systems.

• Write high-quality, maintainable, and well-tested code, primarily in backend languages like Python or Go.

• Ensure a high degree of code quality, security, and system observability.

• Collaborate with editorial, product, ML, and data teams to translate requirements into platform capabilities.

• Review code written by other software engineers on the team and provide feedback.

• Contribute to and advocate for robust testing strategies to ensure service reliability.

• Be a mentor to other software engineers to enable their growth.

• Demonstrate support and understanding of our value of journalistic independence and a strong commitment to our mission to seek the truth and help people understand the world.

Basic Qualifications:

• 5+ years of software engineering experience, with a strong focus on backend development.

• 5+ years of experience in one or more backend programming languages such as Python or Go.

• 5+ years of experience designing, building, and operating distributed systems and APIs at scale.

• 1+ years of experience building applications with Large Language Models (LLMs) and familiarity with the surrounding ecosystem (e.g., orchestration, retrieval-augmented generation).

Preferred Qualifications:

This role requires limited on-call hours. An on-call schedule will be determined when you join, taking into account team size and other variables.

#LI-Hybrid

REQ-019837

About the Role:

Machine Learning (ML) at the New York Times enhances the experience of our 150 million digital readers from around the globe and grows our subscriber base through content recommendations and personalizations.

The Machine Learning & AI team builds and maintains the infrastructure that hosts all of The New York Times real-time ML inference models, including both data and compute. Our partners are Data Scientists that build and deploy their ML models on the ML platform. On the other end, our partners are engineering systems that call these hosted models at scale with low-latency and Service Level Agreements guaranteed by our platform.

As an MLOps Engineer you will partner with product, data science and ML platform engineers to build and maintain the infrastructure that powers the machine learning lifecycle. You will automate and refine the training, deployment, monitoring, and management of our ML models.

This role reports to the Senior Engineering Manager of Data Management Infrastructure.

Responsibilities:

• Build and Automate ML Pipelines: by owning robust CI/CD pipelines for automated model training, validation, deployment, and retraining.

• Productionalize Models: Build the process for packaging, containerizing, and deploying ML models as scalable, low-latency, and highly-available services.

• Monitoring and Operations: Implement and manage comprehensive monitoring for production models, tracking system health, data drift, and model performance degradation.

• Tooling and Infrastructure: Manage and evolve our MLOps toolchain, including model registries, feature stores, experiment tracking systems, and model serving platforms.

• Collaboration and Support: Partner with data scientists to understand model requirements and optimize them for production. Support software engineers in integrating with ML services.

• Best Practices and Governance: Champion and enforce MLOps best practices for reproducibility, versioning (data, code, model), testing, and governance.

• Demonstrate support and understanding of our value of journalistic independence and a strong commitment to our mission to seek the truth and help people understand the world.

Basic Qualifications:

• 2+ years of software engineering or DevOps experience with a focus on MLOps, automation, and infrastructure

• 2+ years of experience programming in Python or Go

• Experience building and managing CI/CD pipelines (e.g., Github Actions, Jenkins, GitLab CI)

• Hands-on experience with containerization and orchestration (e.g., Docker, Kubernetes)

• Cloud platform experience (AWS, GCP) and familiarity with infrastructure-as-code (e.g., Terraform, CloudFormation)

Preferred Qualifications:

• Experience with MLOps tools (e.g., MLflow, Kubeflow)

• Experience with the machine learning model lifecycle, from experimentation to production

• Experience with data processing frameworks (e.g., Spark, Dask, or Ray)

• Experience with low-latency no-sql datastores (BigTable, Dynamo, etc)

• Familiarity with monitoring and observability stacks (e.g., Prometheus, Grafana, Datadog, or ELK)

• Knowledge of data engineering pipelines and orchestration tools (e.g., Airflow, Prefect)

REQ-019522

#LI-hybrid

About the Role

The New York Times is looking for a Senior Software Engineer, with a focus on ML platform development, to join our Machine Learning & AI team to help solve creative challenges around Machine Learning infrastructure.

Machine Learning (ML) at the New York Times enhances the experience of our 150 million digital readers from around the globe and grows our subscriber base through content recommendations and personalizations.

The Machine Learning & AI team builds and maintains the infrastructure that hosts all of The New York Times real-time ML inference models, including both data and compute. Our partners are Data Scientists that build and deploy their ML models on the ML platform. On the other end, our partners are engineering systems that call these hosted models at scale with low-latency and Service Level Agreements (SLAs) guaranteed by our platform.

This role is a hybrid role, based in New York City. You will report to the Engineering Manager of the Machine Learning Platform team.

Responsibilities:

• You will research, develop, and deploy infrastructure for the Machine Learning Platform that supports large-scale multi-tenant workloads

• You will build a platform to train and test algorithms that provide real-time content recommendations and personalization to our readers

• You will enhance ML platform's CI/CD and integration testing capabilities

• You will build a platform that supports the full Machine Learning workflow; from model training, testing and production deployment at high scale and low latency

• Demonstrate support and understanding of our value of journalistic independence and a strong commitment to our mission to seek the truth and help people understand the world

Basic Qualifications:

• 5+ years of direct relevant experience in MLOps or DevOps, including experience operating large systems in a production environment

• Experience deploying and monitoring systems using cloud infrastructure (GCP or AWS)

• Experience working with Kubernetes, Docker, and CI/CD (Drone, Argo, Jenkins, etc)

• Experience leading the development of large-scale, data-driven, distributed multi-tenant systems

• Proficiency with at least one high-level programming language like Python or Go

Preferred Qualifications:

• Experience with any of the these technologies: Terraform, Airflow, SQL/BigQuery, BigTable or other NoSQL datastores such as Cassandra, DynamoDB, Redis

• Familiarity with ML tooling such as Triton, TensorFlow, scikit-learn

• Experience building the infrastructure that power real-world machine learning applications like recommendation systems, bandits, etc.

• Experience engaging with partners to understand pain points, observe patterns, and identify opportunities for improvements

This role requires limited on-call hours. An on-call schedule will be determined when you join, taking into account team size and other variables.

REQ-019502

#LI-hybrid

About the Role

Machine Learning (ML) at the New York Times enhances the experience of our 150 million digital readers from around the globe and grows our subscriber base through content recommendations and personalizations.

The Machine Learning Platform (MLP) team builds and maintains the infrastructure that hosts all of The New York Times real-time ML inference models, including both data and compute. Our partners are Data Scientists that build and deploy their ML models on the ML platform. On the other end, our partners are engineering systems that call these hosted models with low-latency and Service Level Agreements (SLAs) guaranteed by the MLP.

We are looking for a Software Engineer, with a focus on MLOps, to join our Machine Learning Platform team to help solve creative challenges around Machine Learning infrastructure for the New York Times.

This role is a hybrid role, based in New York City. You will report to the Engineering Manager of the Machine Learning Platform team.

Responsibilities:

• You will research, develop, and deploy infrastructure for the Machine Learning Platform at scale

• You will build infrastructure to train and test algorithms that provide real-time content recommendations and personalization

• You will enhance ML platform's CI/CD and integration testing capabilities

• You will promote developer productivity by rolling out capabilities that maximize self-sufficiency and minimize the toil in learning those capabilities in building scalable applications with high speed, quality, and performance

• Demonstrate support and understanding of our value of journalistic independence and a strong commitment to our mission to seek the truth and help people understand the world

Basic Qualifications:

• 2+ years of direct relevant experience in MLOps or DevOps, including experience operating large systems in a production environment

• Experience deploying and monitoring systems using cloud infrastructure (GCP or AWS)

• Experience working with Kubernetes, Docker, and CI/CD

• Familiarity with at least one high-level programming language like Python or Go

Preferred Qualifications:

• Experience with any of the these technologies: Terraform, Airflow, SQL/BigQuery, Drone, Triton, TensorFlow, scikit-learn, DataDog

• Experience building real-world machine learning applications like recommendation systems, bandits, etc.

• Experience engaging with stakeholders to understand pain points, observe patterns, and identify opportunities for improvements

This role may require limited on-call hours. An on-call schedule will be determined when you join, taking into account team size and other variables.

REQ-019501

#LI-Hybrid

AI Software Engineer

The Team + The Role

Our Emerging Team is focused on building AI Products for our product experience (PX) platform. We build from the ground up to explore, prototype, and ship AI-native experiences that change how software teams understand and serve their users. This is not an AI layer added to existing product; it is a deliberate bet on what product intelligence looks like next. The team operates with high autonomy, moves quickly, and builds products without clear precedents.

As an AI Software Engineer, you will build applied AI systems that move from prototype to production. You will bring deep technical capability, strong product judgment, and clear communication to decisions across prompting, fine-tuning, RAG, MLOps, and full-stack product delivery. You will partner closely with product and design to frame the right problems and ship AI-native experiences that hold up for real users.

This role is based in our New York office.

What this looks like day-to-day

• Applied AI systems: Design and build AI-native product experiences, including RAG pipelines, agentic workflows, and LLM-powered features. You will take work from prototype through production and ensure systems are reliable enough for real users.
• Technical decision-making: Make principled decisions about when to prompt, when to fine-tune, and when to use a different technical approach. You will explain those tradeoffs clearly to engineers and non-engineers.
• Model evaluation: Instrument and evaluate model outputs rigorously by defining evaluation frameworks and identifying failure modes early. You will catch hallucinations, measure quality, and implement guardrails that hold up under real-world load.
• MLOps ownership: Own model deployment, monitoring, latency optimization, cost management, and reliability at scale. You will ensure AI systems operate effectively in live production environments.
• Full-stack product delivery: Contribute across the stack when needed because the team ships products, not just models. You will help get complete user-facing AI experiences into customers’ hands.
• Product partnership: Partner closely with product and design to frame problems before implementation begins. You will push back when the framing is wrong and help the team focus on what should be built, not only how to build it.
• Research and tooling awareness: Stay current on the research and tooling landscape, including transformers, diffusion architectures, orchestration frameworks, and emerging agent patterns. You will bring relevant advances back to the team and apply them thoughtfully.

Who You Are

Beyond the qualifications, we hire through a specific lens. These aren't buzzwords; they're the things we'll actually look for in how you talk about your work.

You're a builder, not a maintainer.

You're most energized when there isn't a clear path yet, and you get to define it. You don't wait for direction; you identify gaps, shape solutions, and drive them forward. At Pendo, great AI Software Engineers don't just follow instructions; they operate as strategic advisors, influencing decisions, guiding stakeholders, and elevating how we work.

You're AI-curious - genuinely.

You're not using AI tools occasionally. You're rewiring how you work around them. You're faster, sharper, and more prolific because of it, and you bring that energy to everything — how you approach your work, how you prep, how you communicate, how you think. We want someone who sees AI as a multiplier, not a shortcut.

Must-haves

• Deep hands-on experience building and shipping LLM-powered systems, including retrieval-augmented generation, tool use, and agent orchestration frameworks.
• Demonstrated ability to apply established AI engineering patterns to well-scoped problems and ship them reliably in production.
• End-to-end ownership of features from design through deployment and monitoring within a defined problem space.
• Strong command of model evaluation, including designing evaluation suites, reasoning about overfitting and bias-variance tradeoffs, and systematically detecting and mitigating hallucinations.
• Solid understanding of modern model architectures, including transformers and diffusion models, with the ability to make informed decisions about when and how to apply them.
• Production MLOps experience, including model deployment, monitoring pipelines, and latency, cost, and reliability optimization in a live environment.
• Strong full-stack fundamentals and comfort working across backend and frontend systems to ship complete user-facing AI products.
• Exceptional communication skills, with the ability to explain complex technical decisions clearly to engineers, product managers, and executives.
• Demonstrated product thinking, including the judgment to ask whether something should be built before deciding how to build it.

Nice-to-haves

• Experience fine-tuning foundation models and a clear point of view on when fine-tuning outperforms prompting approaches.
• Familiarity with AI safety considerations, guardrail frameworks, and responsible deployment practices.
• Experience building agentic or multi-step reasoning systems using tools such as LangChain, LlamaIndex, or custom orchestration frameworks.
• Background in a SaaS or product analytics environment where user behavior data informs AI design.
• Prior experience contributing to or launching a net-new team or product area.

About Pendo

Pendo was founded in 2013 by former product managers, who combined their heads and hearts to build something they wanted but never had as product managers: a simple way to understand and attack what truly drives product success. Our mission is to improve society's experience with software. Come join one of the fastest-growing startups, supported by best-in-class institutions like Battery Ventures, Salesforce Ventures, Spark Capital and Meritech.

Pendo Core Values: Bias to Act, Hone Your Craft, The Team is Pendo, and Maniacal Focus.

Location: Pendo is a hybrid culture. In-office 3 days per week unless designated remote.

Compensation: The expected salary range for this role to be performed in New York, NY is $178,000 - $195,000.

Benefits: Highly competitive, employer-heavy coverage including $0 premium options, strong 401(k) match, equity, and flexible time off.

EEOC: We are an equal opportunity employer and believe having diverse teams where everyone brings their whole self to Pendo is key to our success. We welcome all people of different backgrounds, experiences, abilities and perspectives.

Accessibility: Pendo is committed to working with, and providing access and reasonable accommodation to, applicants with mental and/or physical disabilities. If you think you may require an accommodation for any part of the recruitment process, please send a request to: accommodation@pendo.io. All requests for accommodations are treated discreetly and confidentially, as practical and permitted by law.

Sr. AI Software Engineer

The Team + The Role

Our Emerging Team is focused on building AI Products for our product experience (PX) platform. We build from the ground up to explore, prototype, and ship AI-native experiences that change how software teams understand and serve their users. This is not an AI layer added to existing product; it is a deliberate bet on what product intelligence looks like next. The team operates with high autonomy, moves quickly, and builds products without clear precedents.

As a Sr. AI Software Engineer, you will sit at the intersection of deep technical capability and strong product judgment. You will design and ship applied AI systems, including RAG pipelines, agentic workflows, and LLM-powered features, from prototype through production. You will make principled technical decisions, evaluate model behavior rigorously, and communicate tradeoffs clearly to engineers and non-engineers alike.

This role is based in our New York office.

What this looks like day-to-day

• Applied AI systems: Design and build AI systems including RAG pipelines, agentic workflows, and LLM-powered features. You will take work from prototype through production and ensure it can hold up in real customer environments.
• Technical decision-making: Make principled decisions on when to prompt, when to fine-tune, and when to use a different tool entirely. You will explain these tradeoffs clearly so the team can move quickly without sacrificing quality.
• Model evaluation: Instrument and evaluate model outputs rigorously by defining evaluation frameworks and catching hallucinations early. You will implement guardrails that can withstand real-world load and production use.
• MLOps ownership: Own model deployment, monitoring, latency optimization, cost management, and reliability at scale. You will help ensure AI systems are observable, efficient, and dependable in production.
• Full-stack product shipping: Contribute across the stack when needed because this team ships products, not just models. You will work across backend and frontend to get AI-powered experiences in front of users.
• Product partnership: Partner closely with product and design to frame problems well before writing code. You will push back when the framing is wrong and help the team focus on what should be built, not just what can be built.
• Research and tooling awareness: Stay current on the research and tooling landscape, including transformers, diffusion architectures, orchestration frameworks, and emerging agent patterns. You will bring relevant advances back to the team and apply them thoughtfully.

Who You Are

Beyond the qualifications, we hire through a specific lens. These aren't buzzwords; they're the things we'll actually look for in how you talk about your work.

You're a builder, not a maintainer.

You're most energized when there isn't a clear path yet, and you get to define it. You don't wait for direction; you identify gaps, shape solutions, and drive them forward. At Pendo, great Sr. AI Software Engineers don't just follow instructions; they operate as strategic advisors, influencing decisions, guiding stakeholders, and elevating how we work.

You're AI-curious - genuinely.

You're not using AI tools occasionally. You're rewiring how you work around them. You're faster, sharper, and more prolific because of it, and you bring that energy to everything — how you approach your work, how you prep, how you communicate, how you think. We want someone who sees AI as a multiplier, not a shortcut.

Must-haves

• Deep hands-on experience building and shipping LLM-powered systems, including retrieval-augmented generation, tool use, and agent orchestration frameworks.
• Strong technical depth in system design, including choosing the right architecture, identifying failure modes early, and making tradeoffs that hold up across the product lifecycle.
• Experience owning technical quality beyond your own features, including setting standards, catching problems in review, and improving shared infrastructure and tooling.
• Strong command of model evaluation, including designing evaluation suites, reasoning about overfitting and bias-variance tradeoffs, and systematically detecting and mitigating hallucinations.
• Solid understanding of modern model architectures, including transformers and diffusion models, with the judgment to decide when and how to apply them.
• Production MLOps experience, including model deployment, monitoring pipelines, and latency, cost, and reliability optimization in a live environment.
• Strong full-stack fundamentals with the ability to work across backend and frontend systems to ship complete, user-facing AI products.
• Exceptional communication skills with the ability to explain complex technical decisions clearly to engineers, product managers, and executives.
• Demonstrated product thinking, including the ability to ask whether something should be built before deciding how to build it.

Nice-to-haves

• Experience fine-tuning foundation models and a clear point of view on when fine-tuning outperforms prompting approaches.
• Familiarity with AI safety considerations, guardrail frameworks, and responsible deployment practices.
• Experience building agentic or multi-step reasoning systems using tools such as LangChain, LlamaIndex, or custom orchestration frameworks.
• Background in a SaaS or product analytics environment where user behavior data informs AI design.
• Prior experience contributing to or launching a net-new team or product area.

About Pendo

Pendo was founded in 2013 by former product managers, who combined their heads and hearts to build something they wanted but never had as product managers: a simple way to understand and attack what truly drives product success. Our mission is to improve society's experience with software. Come join one of the fastest-growing startups, supported by best-in-class institutions like Battery Ventures, Salesforce Ventures, Spark Capital and Meritech.

Pendo Core Values: Bias to Act, Hone Your Craft, The Team is Pendo, and Maniacal Focus.

Location: Pendo is a hybrid culture. In-office 3 days per week unless designated remote.

Compensation: The expected salary range for this role to be performed in New York, NY is $209,000 - $235,000.

Benefits: Highly competitive, employer-heavy coverage, including $0 premium options, strong 401(k) match, equity, and flexible time off.

EEOC: We are an equal opportunity employer and believe having diverse teams where everyone brings their whole self to Pendo is key to our success. We welcome all people of different backgrounds, experiences, abilities and perspectives.

Accessibility: Pendo is committed to working with, and providing access and reasonable accommodation to, applicants with mental and/or physical disabilities. If you think you may require an accommodation for any part of the recruitment process, please send a request to: accommodation@pendo.io. All requests for accommodations are treated discreetly and confidentially, as practical and permitted by law.

Staff AI Software Engineer

The Team + The Role

Our Emerging Team is focused on building AI Products for our product experience (PX) platform. We build from the ground up to explore, prototype, and ship AI-native experiences that change how software teams understand and serve their users. This is not an AI layer added to existing product; it is a deliberate bet on what product intelligence looks like next. The team operates with high autonomy, moves quickly, and builds products without clear precedents.

As a Staff AI Software Engineer, you will sit at the intersection of deep technical capability and strong product judgment. You will design and build production-grade AI systems, including RAG pipelines, agentic workflows, and LLM-powered features, while making clear tradeoffs across prompting, fine-tuning, architecture, evaluation, and deployment. You will also partner closely with product, design, and engineering stakeholders to frame the right problems and communicate technical decisions clearly.

This role is based in our New York office.

What this looks like day-to-day

• Applied AI systems: Design and build AI-native systems, including RAG pipelines, agentic workflows, and LLM-powered product features. You will take ideas from prototype through production and ensure they can support real users.
• Model strategy: Make principled decisions about when to prompt, when to fine-tune, and when to use a different technical approach entirely. You will explain those tradeoffs clearly to engineers and non-engineers.
• Evaluation and guardrails: Instrument and evaluate model outputs rigorously by defining evaluation frameworks and identifying hallucinations early. You will implement guardrails that hold up under real-world usage and load.
• MLOps ownership: Own model deployment, monitoring, latency optimization, cost management, and reliability at scale. You will ensure AI systems are observable, performant, and production-ready.
• Full-stack delivery: Contribute across the stack when needed to get complete AI products in front of users. This team ships products, not just models, and you will help close the gap between technical capability and user experience.
• Product partnership: Partner closely with product and design to frame problems well before implementation begins. You will push back when the framing is wrong and help the team stay focused on what is worth building.
• Technical leadership: Stay current on the research and tooling landscape, including transformers, diffusion architectures, orchestration frameworks, and emerging agent patterns. You will bring relevant advances back to the team and help raise the technical bar.

Who You Are

Beyond the qualifications, we hire through a specific lens. These aren't buzzwords; they're the things we'll actually look for in how you talk about your work.

You're a builder, not a maintainer.

You're most energized when there isn't a clear path yet, and you get to define it. You don't wait for direction; you identify gaps, shape solutions, and drive them forward. At Pendo, great Staff AI Software Engineers don't just follow instructions; they operate as strategic advisors, influencing decisions, guiding stakeholders, and elevating how we work.

You're AI-curious - genuinely.

You're not using AI tools occasionally. You're rewiring how you work around them. You're faster, sharper, and more prolific because of it, and you bring that energy to everything — how you approach your work, how you prep, how you communicate, how you think. We want someone who sees AI as a multiplier, not a shortcut.

Must-haves

• Deep hands-on experience building and shipping LLM-powered systems, including retrieval-augmented generation, tool use, and agent orchestration frameworks.
• Demonstrated ability to set technical direction for AI systems across teams, establish architectural patterns, make foundational model strategy decisions, and raise the bar for AI engineering quality.
• Experience owning outcomes across team boundaries, including identifying capability gaps, driving alignment across engineering and product, and influencing how a broader organization approaches AI.
• Strong command of model evaluation, including designing evaluation suites, reasoning about overfitting and bias-variance tradeoffs, and systematically detecting and mitigating hallucinations.
• Solid understanding of modern model architectures, including transformers and diffusion models, with the ability to make informed decisions about when and how to apply them.
• Production MLOps experience, including model deployment, monitoring pipelines, and latency, cost, and reliability optimization in live environments.
• Strong full-stack fundamentals and comfort working across backend and frontend systems to ship complete, user-facing AI products.
• Exceptional communication skills, with the ability to explain complex technical decisions clearly to engineers, product managers, and executives.
• Demonstrated product judgment and the ability to evaluate whether something should be built before determining how to build it.

Nice-to-haves

• Experience fine-tuning foundation models and a clear point of view on when fine-tuning outperforms prompting-based approaches.
• Familiarity with AI safety considerations, guardrail frameworks, and responsible deployment practices.
• Experience building agentic or multi-step reasoning systems using tools such as LangChain, LlamaIndex, or custom orchestration frameworks.
• Background in a SaaS or product analytics environment where user behavior data informs AI product design.
• Prior experience contributing to or launching a net-new team or product area.

About Pendo

Pendo was founded in 2013 by former product managers, who combined their heads and hearts to build something they wanted but never had as product managers: a simple way to understand and attack what truly drives product success. Our mission is to improve society's experience with software. Come join one of the fastest-growing startups, supported by best-in-class institutions like Battery Ventures, Salesforce Ventures, Spark Capital and Meritech.

Pendo Core Values: Bias to Act, Hone Your Craft, The Team is Pendo, and Maniacal Focus.

Location: Pendo is a hybrid culture. In-office 3 days per week unless designated remote.

Compensation: The expected salary range for this role to be performed in New York, NY is $250,000 - $280,000.

Benefits: Highly competitive, employer-heavy coverage, including $0 premium options, strong 401(k) match, equity, and flexible time off.

EEOC: We are an equal opportunity employer and believe having diverse teams where everyone brings their whole self to Pendo is key to our success. We welcome all people of different backgrounds, experiences, abilities and perspectives.

Accessibility: Pendo is committed to working with, and providing access and reasonable accommodation to, applicants with mental and/or physical disabilities. If you think you may require an accommodation for any part of the recruitment process, please send a request to: accommodation@pendo.io. All requests for accommodations are treated discreetly and confidentially, as practical and permitted by law.

Octus is seeking a Principal Data Engineer to roll up their sleeves and build scalable, production-grade data pipelines and infrastructure. You'll be a hands-on technical leader — writing code daily, solving hard engineering problems, and helping elevate the team around you through doing. Spanning Snowflake, Databricks, and AWS, you'll be deeply involved in the day-to-day development of the data platform that powers Octus's products, data, and automation initiatives. The ideal candidate is an expert in Python and SQL who thrives in an execution-focused environment and has deep experience building modern data pipelines and lakehouse solutions.

Responsibilities

• Build and maintain end-to-end data pipelines — from raw ingestion through transformation and delivery — across diverse data sources (APIs, web data, internal feeds, etc.).
• Hands-on development of scalable, production-grade pipelines within Databricks, including Delta Lake table management, Workflows, and cluster optimization.
• Build and maintain data models, schemas, and transformation logic in Snowflake, optimizing for performance and reliability.
• Develop and manage Databricks environments including Unity Catalog, Delta Live Tables, and integration patterns that support both internal data consumers and external sharing use cases.
• Build and manage orchestration workflows using AWS services (MWAA/Airflow, Lambda, ECS, SQS, MSK) and Databricks-native orchestration where appropriate.
• Implement and maintain infrastructure as code (IaC) using Terraform, ensuring reproducibility and compliance with cloud standards.
• Establish and enforce best practices in data modeling, schema design, and ETL/ELT processes for high-volume structured and semi-structured data across Snowflake and Databricks.
• Ensure data quality, lineage, and observability through automated testing, monitoring, and alerting across all pipeline layers.
• Collaborate closely with technology leadership to align data platform development with business strategy and product goals.
• Stay at the forefront of industry trends in data engineering, lakehouse architecture, and cloud-native data platforms.

Requirements

• Strong foundation in software engineering principles, including SOLID design, modularity, and scalability.
• Expert proficiency in Databricks, including Delta Lake, Unity Catalog, Delta Live Tables, MLflow, and Databricks Workflows.
• Deep experience with Snowflake, including data modeling, performance optimization, and integration with upstream/downstream pipeline tooling.
• Expert proficiency in Python for data pipeline and automation development.
• Advanced SQL skills with experience optimizing complex queries and data models at scale.
• Proven experience designing and maintaining cloud-native data pipelines on AWS (e.g., MWAA/Airflow, Lambda, ECS, SQS, Glue, S3, Redshift, etc.).
• Experience implementing and managing Terraform or similar IaC frameworks.
• Strong understanding of lakehouse architecture patterns, data ingestion, transformation, and orchestration, including familiarity with ML/AI pipeline integration patterns.
• Familiarity with CI/CD pipelines, automated testing, and modern DevOps practices.
• 8+ years of experience in data engineering or backend development, with a focus on scalable data solutions.
• Demonstrated experience leading data infrastructure projects end-to-end and mentoring senior engineers.
• Familiarity with containerization (Docker) and workflow orchestration best practices.
• Excellent communication, collaboration, and problem-solving skills.

Nice to Have

• Experience with streaming data technologies (Kafka, Kinesis, Flink).
• Exposure to ML/AI pipeline patterns (feature stores, experiment tracking, model serving) and MLOps tooling, particularly in a cross-functional team environment.
• Experience integrating data quality and observability tools.
• Experience with Databricks as a data sharing and collaboration platform (Delta Sharing, Marketplace).
• Familiarity with Claude Code or similar AI-powered developer tools for accelerating pipeline development and code workflows.

At Octus, we consider a range of factors in connection with compensation decisions, including experience, skills, location, and our business needs and limitations. As a result, compensation may vary within and across similar roles and positions. Please note that the salary range information below is a good faith estimate for this position and actual compensation for any individual may fall outside this range if warranted by the circumstances applicable to that individual. If we identify a role that would be suitable for a broader range of skills and experience such that we would consider hiring at multiple levels then the range listed below may reflect that breadth.

The salary range estimate for this position is $175,000 - $220,000

The actual compensation will be at Octus' sole discretion and will be determined by the aforementioned and other relevant factors.

The Data Science team at Asana is pivotal in fulfilling our mission by fostering a data-driven approach in shaping both our product and business strategies. In your role on the Marketing Data Science team, you will be the deepest technical expert responsible for using data and scientific techniques to design and build scalable, state-of-the-art solutions to enhance Asana’s marketing effectiveness. You will drive the technical roadmap for data science, collaborating with marketing leadership and the broader Asana data community to uncover new opportunities. You will provide technical leadership and hands-on mentorship, elevating the team's technical bar and influencing overall business strategy through best-in-class modeling and experimental design.

This role is based in our New York City office with an office-centric hybrid schedule. The standard in-office days are Monday, Tuesday, and Thursday. Most Asanas have the option to work from home on Wednesdays. Working from home on Fridays depends on the type of work you do and the teams with which you partner. If you're interviewing for this role, your recruiter will share more about the in-office requirements.

What you’ll achieve:

• Architect, design, and lead the technical execution for the Marketing Data Science roadmap, serving as the Solution Architect for all core projects including Media Mix Modeling (MMM), User Lifetime Value, Causal Inferences, Multi-touch Attribution, and Spend Optimization engines.
• Act as the primary technical subject matter expert for the Marketing Data Science team, setting the technical bar for modeling quality, code rigor, data pipeline architecture, and solution scalability.
• Collaborate with marketing leadership to pinpoint how data science can be further integrated into Asana's business approach.
• Provide hands-on technical mentorship and guidance to a team of data scientists at varying levels, helping them navigate complex modeling challenges, choose appropriate methodologies, and establish robust ML Ops.
• Develop and standardize MLOps tooling and processes that enable the team to deploy, monitor, and maintain multiple models in production efficiently and reliably.
• Research, prototype, and advocate for emerging capabilities and state-of-the-art models in the marketing data science space, demonstrating their potential benefits and leading their implementation.
• Take on a technical leadership role within the broader Asana Data Community, interacting with Data Engineering and Platform teams to influence the data and MLOps infrastructure required to support marketing data products.

About you:

• Bachelor Degree in Math, Statistics, Computer Science, Engineering a related quantitative field, or equivalent experience
• 6+ years of experience in a data science role, with 2+ years dedicated to technical leadership and mentorship of other data scientists, successfully driving the architecture and execution of large-scale production data science projects
• 4+ years of experience collaborating with Marketing functions on deep technical projects, with extensive experience designing, implementing, and deploying marketing models (e.g. MMM, LTV, MTA, Uplift)
• Expert-level knowledge in advanced statistical modeling, causal inference, experimental design and analysis, and machine learning techniques relevant to marketing effectiveness
• Proven track record developing, deploying, and maintaining scalable production ML solutions and data products
• Technical Stack: Expert proficiency in SQL and Python. Experience with MLOps tools (e.g., MLFlow), statistical languages (e.g., R), and distributed data processing systems (e.g., Spark, Redshift) is a plus
• Demonstrates curiosity about AI tools and emerging technologies, with a willingness to learn and leverage them to enhance productivity, collaboration, or decision-making.

What we’ll offer:

Our comprehensive compensation package plays a big part in how we recognize you for the impact you have on our path to achieving our mission. We believe that compensation should be reflective of the value you create relative to the market value of your role. To ensure pay is fair and not impacted by biases, we're committed to looking at market value which is why we check ourselves and conduct a yearly pay equity audit.

For this role, the estimated base salary range is between $202,000 - $282,000. The actual base salary will vary based on various factors, including market and individual qualifications objectively assessed during the interview process. The listed range above is a guideline, and the base salary range for this role may be modified.

In addition to base salary, your compensation package may include additional components such as equity, sales incentive pay (for most sales roles), and benefits. If you're interviewing for this role, speak with your Talent Acquisition Partner to learn more about the total compensation and benefits for this role.

We strive to provide equitable and competitive benefits packages that support our employees worldwide and include:

• Mental health, wellness & fitness benefits
• Career coaching & support
• Inclusive family building benefits
• Long-term savings or retirement plans
• In-office culinary options to cater to your dietary preferences 

These are just some of the benefits we offer, and benefits may vary based on role, country, and local regulations. If you're interviewing for this role, speak with your Talent Acquisition Partner to learn more about the total compensation and benefits for this role.

#LI-Hybrid #LI-AA1

CoreWeave, the AI Hyperscaler™, acquired Weights & Biases to create the most powerful end-to-end platform to develop, deploy, and iterate AI faster. Since 2017, CoreWeave has operated a growing footprint of data centers covering every region of the US and across Europe, and was ranked as one of the TIME100 most influential companies of 2024. By bringing together CoreWeave’s industry-leading cloud infrastructure with the best-in-class tools AI practitioners know and love from Weights & Biases, we’re setting a new standard for how AI is built, trained, and scaled.

The integration of our teams and technologies is accelerating our shared mission: to empower developers with the tools and infrastructure they need to push the boundaries of what AI can do. From experiment tracking and model optimization to high-performance training clusters, agent building, and inference at scale, we’re combining forces to serve the full AI lifecycle — all in one seamless platform.

Weights & Biases has long been trusted by over 1,500 organizations — including Nvidia, Canva, Cohere, Meta, Snowflake, Square, Toyota, and Amazon — to build better models, AI agents and applications. Now, as part of CoreWeave, that impact is amplified across a broader ecosystem of AI innovators, researchers, and enterprises.

As we unite under one vision, we’re looking for bold thinkers and agile builders who are excited to shape the future of AI alongside us. If you're passionate about solving complex problems at the intersection of software, hardware, and AI, there's never been a more exciting time to join our team.

About the Role:

As an AI Solutions Engineer you will sit at the intersection of cutting-edge AI infrastructure and the world’s most ambitious AI teams. You will help customers design, deploy, and scale modern ML and GenAI systems using Weights & Biases and CoreWeave’s AI cloud. This role combines hands-on technical depth with strategic customer engagement — ideal for AI practitioners who enjoy building real systems, solving complex problems, and shaping how AI is developed and operationalized at scale.

What You’ll Do:

• Own the technical wins by building proof of value (PoVs). You’ll be partnering with Account Executives to drive discovery, solution design, and successful technical evaluations.
• Lead deep technical conversations with ML engineers, researchers, and platform teams to understand their AI/GenAI workloads and architect scalable solutions across the CoreWeave and Weights & Biases ecosystem .
• Design and deliver compelling live demos, proof-of-value engagements, and rapid prototypes showcasing experiment tracking, distributed training, agentic workflows, fine-tuning, evaluation, and inference at scale.
• Architect modern ML and GenAI systems including distributed training, RAG pipelines, multi-agent systems, LLM fine-tuning workflows, and inference solutions.
• Translate business objectives into measurable technical outcomes such as model quality improvements, cost reductions, performance gains, and faster iteration cycles.
• Serve as a trusted technical advisor, influencing architectural decisions and best practices across MLOps, LLMOps, and platform engineering.
• Partner closely with Product and Engineering to relay customer feedback, shape roadmap priorities, and improve the end-to-end AI developer experience.

Who You Are:

• 2+ years of experience in Solutions Engineering, ML Engineering, AI Engineering, or a similar customer-facing technical role.
• Bachelors degree in Computer Science, Data Science, Electrical Engineering, Statistics, Mathematics, or related field. Master in similar area of study preferred.
• Strong proficiency in Python (bonus: TypeScript/JavaScript) and experience building production-grade ML or GenAI applications.
• Hands-on experience training, fine-tuning, evaluating, debugging, and deploying deep learning or LLM-based systems.
• Experience with modern GenAI architectures including retrieval-augmented generation (RAG), tool-using agents, evaluation frameworks, and guardrails.
• Comfortable designing cloud-native architectures across AWS, GCP, or Azure, with working knowledge of containers, networking fundamentals, and security best practices.
• Able to whiteboard distributed training or inference architectures and then implement working prototypes that demonstrate real customer value.
• Excellent communication and storytelling skills, with the ability to translate complex technical concepts into business impact.
• Thrives in fast-paced environments and can manage multiple customer engagements simultaneously.

Preferred:

• Experience building and evaluating agentic applications using frameworks such as LangChain, DSPy, LlamaIndex, CrewAI, OpenClaw, or similar ecosystems. Ability to quickly adapt to new frameworks is a huge plus.
• Familiarity with vector databases, embeddings workflows, and LLM evaluation frameworks (e.g., Ragas, TruLens, DeepEval).
• Hands-on experience with Hugging Face Transformers, PEFT/LoRA/QLoRA fine-tuning, TRL, Unsloth, or similar fine-tuning stacks. Big plus if experience using RL to improve agentic systems. 
• Experience operating distributed workloads using Slurm, Ray, or Kubernetes.
• Experience with inference optimization and serving frameworks such as vLLM, TensorRT-LLM, Triton, or TGI.
• Experience in MLOps/LLMOps platform engineering or supporting enterprise AI deployments.

The base salary range for this role is $165,000 to $242,000. The starting salary will be determined based on job-related knowledge, skills, experience, and market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility).

What You’ll Do
The Field Engineering team at Weights & Biases plays a vital role in ensuring customer success and adoption of our platform. As part of this team, we partner with Sales, Support, Product, and Engineering to lead technical success after the sales process. We work closely with some of the most advanced AI teams in the world, helping them build, optimize, and scale their ML and GenAI workflows across industries such as computer vision, robotics, natural language processing, and large language models (LLMs).

About the Role

We’re hiring a Post-Sales AI Solutions Engineer (AISE), Scale Customer Success to help customers successfully implement and scale AI/ML workflows and GenAI/agentic applications on Weights & Biases. You will design and deliver technical enablement and adoption programs that reach many customers at once, create reusable assets that improve self-serve success, and use product signals and feedback loops to continuously improve outcomes.

Key Responsibilities 

1. Run 1-to-many onboarding and enablement programs
   Own and deliver scalable onboarding and adoption motions (webinars, cohort sessions, group training, and office hours) that help customers get to value quickly and consistently.
2. Build reusable technical assets that drive self-serve success
   Create and maintain playbooks, reference architectures, templates, sample code/notebooks, and troubleshooting guides that standardize best practices and reduce repeated 1:1 work.
3. Operate the scaled motion using signals and feedback loops
   Use product usage signals and customer patterns to segment and trigger the right interventions, track program impact (activation, time-to-first-value, feature adoption), and feed recurring insights back to Support, Product, and Field Engineering to continuously improve the scaled journey.

Who You Are

• 3–5 years of relevant experience in a similar role
• Strong programming proficiency in Python
• Experience with deep learning frameworks (TensorFlow/Keras, PyTorch Lightning) and tools (e.g., Streamlit, LangChain)
• Familiarity with cloud platforms (AWS, GCP, Azure)
• Excellent communication and presentation skills, both written and verbal
• Organized and outcomes-driven: you can run programs, measure impact, and iterate.

Preferred

• Experience with GenAI and LLMs
• Proficiency with HuggingFace, Fastai, scikit-learn, XGBoost, LightGBM, or Ray
• Experience with hyperparameter optimization solutions
• Background in data engineering, MLOps, or LLMOps, with tools such as Docker and Kubernetes
• Familiarity with data pipeline tools

Wondering if you’re a good fit?
We believe in investing in our people, and value candidates who can bring their own diversified experiences to our teams – even if you aren't a 100% skill or experience match. Here are a few qualities we’ve found compatible with our team. If some of this describes you, we’d love to talk.

• You love collaborating with top AI teams to solve real-world ML and GenAI challenges
• You’re curious about how to optimize AI workflows at scale
• You’re an expert in Python and ML tooling, with a passion for enabling customer success

Why Us?
We work hard, have fun, and move fast! We’re in an exciting stage of hyper-growth that you will not want to miss out on. We’re not afraid of a little chaos, and we’re constantly learning. Our team cares deeply about how we build our product and how we work together, which is represented through our core values:

• Be Curious at Your Core
• Act Like an Owner
• Empower Employees
• Deliver Best-in-Class Client Experiences
• Achieve More Together

We support and encourage an entrepreneurial outlook and independent thinking. We foster an environment that encourages collaboration and provides the opportunity to develop innovative solutions to complex problems. As we get set for takeoff, the growth opportunities within the organization are constantly expanding. You will be surrounded by some of the best talent in the industry, who will want to learn from you, too. Come join us!

The base salary range for this role is $139,000 to $204,000. The starting salary will be determined based on job-related knowledge, skills, experience, and market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility).

What You’ll Do:

The Account Manager owns the commercial and relationship aspects of the post-sales journey across a portfolio of Digital Native and select Enterprise customers. You will drive renewals, identify and close upsell and cross-sell opportunities, and ensure customers achieve measurable adoption outcomes with Weights & Biases (W&B). You’ll partner closely with Field Engineering (FE)—who leads technical success—while you lead the commercial motions including renewal execution, usage-to-value alignment, growth pipeline creation, and multi-threaded stakeholder engagement.

About the role:

You will own renewals and lead commercial execution across your assigned accounts, including identifying and closing upsell and cross-sell opportunities. You will partner closely with Field Engineering (FE) while running the commercial motions: renewal execution, usage-to-value alignment, growth pipeline creation, and multi-threaded stakeholder engagement. You will maintain detailed account plans (account maps, whitespace, usage trends, risks, and growth opportunities) and maintain weekly forecasting accuracy and CRM hygiene across renewals and growth motions. This role requires comfort engaging highly technical personas (ML engineers, researchers, PhDs) and operating with autonomy in a rapidly evolving AI ecosystem; it is not a playbook-driven role.

• Own renewals, upsells, and cross-sells across your assigned accounts.
• Build and maintain detailed account plans including account maps, whitespace, usage trends, risks, and growth opportunities.
• Generate growth pipeline by identifying new use cases, teams, and product opportunities within existing accounts.

Who You Are:

• Based in the Bay Area (San Francisco, Peninsula, South Bay) strongly preferred; secondary options: New York City or Northern NJ.
• 6+ years in B2B/SaaS customer-facing roles commercializing technical products.
• 3+ years in a quota-carrying Account Management, Sales, or Renewals role owning post-sales outcomes.
• Strong, genuine interest in AI/ML and the evolving machine learning ecosystem.
• High technical and product curiosity; comfortable speaking with developers, ML engineers, and researchers.
• Proven ability to drive growth motions (upsells, cross-sells) and manage retention in technical accounts.
• Strong operational discipline: forecasting accuracy, CRM hygiene, renewal cadence management.
• Thrives in ambiguity; able to self-direct and operate independently without detailed playbooks.

Preferred:

• Experience working with ML, MLOps, DevOps, or data infrastructure teams.
• Familiarity with Git, Jupyter, Python, PyTorch, or cloud platforms (AWS, GCP, Azure).
• Experience designing or executing account activation or expansion strategies.
• Exposure to AI-native companies, model builders, or generative AI workflows.

Wondering if you’re a good fit?

We believe in investing in our people, and value candidates who can bring their own diversified experiences to our teams – even if you aren’t a 100% skill or experience match. Here are a few qualities we’ve found compatible with our team. If some of this describes you, we’d love to talk.

• You love owning renewals and growth motions while building multi-threaded stakeholder relationships.
• You’re curious about AI/ML and enjoy learning quickly in a rapidly evolving ecosystem.
• You’re an expert in operating with strong discipline across account plans, forecasting, and CRM hygiene to drive commercial outcomes.

The base salary range for this role is $95,000 to $130,000. The starting salary will be determined based on job-related knowledge, skills, experience, and market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility).

About the Role:

CoreWeave is seeking a Staff AI Security Engineer to define and operationalize security across our AI ecosystem. This role sits at the intersection of AI/ML, cloud infrastructure, and security engineering. You'll build secure-by-default foundations for AI development, agentic workflows, and enterprise AI adoption, balancing velocity with strong security guarantees. You will also partner closely with enterprise security and IT teams to manage AI-adjacent risks across the corporate environment, including shadow AI, data exfiltration through AI tooling, and SaaS security posture for AI-enabled platforms.

What You'll Do:

• Design and implement security controls across AI/ML infrastructure, including model artifact storage, data lineage and integrity, model signing and provenance, and ML pipeline security (MLOps/MLSecOps)
• Develop AI security policies, standards, and threat models covering model development, training pipelines, data ingestion, inference environments, and agentic systems
• Build security guardrails for agentic workflows: tool access and permissioning, input/output validation, execution boundaries, sandboxing, and auditability of agent actions
• Lead secure adoption of AI tools across engineering, security, operations, and enterprise functions. Evaluate AI vendors, copilots, and integrations. Define policies for data sharing, model usage boundaries, and sensitive data handling in prompts and outputs
• Partner with enterprise security and IT on AI-related risks across the corporate environment, including endpoint posture, corporate identity, and SaaS security for AI-enabled platforms
• Conduct threat modeling and adversarial testing of AI systems, covering prompt injection, data poisoning, model extraction, and backdoored models. Build and maintain a CoreWeave-specific AI threat taxonomy
• Drive cross-team technical decisions and influence architecture across platform engineering, security engineering, ML/AI teams, and product/DevEx to embed security into AI platforms and developer tooling

Who You Are:

Minimum Qualifications

• 10+ years of experience in security engineering (cloud, application, or infrastructure)
• Experience with AI/ML systems: LLMs, training pipelines, inference systems, or MLOps
• Strong, demonstrable experience building and securing large-scale distributed systems
• Strong coding skills in Go, Python, or similar
• Familiarity with Kubernetes, containerized environments, and cloud platforms (AWS, GCP, Azure)
• Understanding of AI-specific threats: prompt injection, data leakage, model misuse, supply chain risks in models and datasets
• Track record of driving cross-team technical initiatives and influencing architecture decisions at the org level

Preferred:

• Experience building or securing LLM-based systems, agent frameworks (LangChain, etc.), or AI-powered internal tools
• Background in adversarial ML or red teaming AI systems
• Experience with secure model deployment pipelines or confidential computing / secure enclaves
• Familiarity with identity and access control systems
• Experience in high-performance or GPU-centric environments
  
  

Why CoreWeave?

At CoreWeave, we work hard, have fun, and move fast!  We’re in an exciting stage of hyper-growth that you will not want to miss out on. We’re not afraid of a little chaos, and we’re constantly learning. Our team cares deeply about how we build our product and how we work together, which is represented through our core values: 

• Be Curious at Your Core
• Act Like an Owner
• Empower Employees
• Deliver Best-in-Class Client Experiences
• Achieve More Together

We support and encourage an entrepreneurial outlook and independent thinking. We foster an environment that encourages collaboration and enables the development of innovative solutions to complex problems. As we get set for takeoff, the organization's growth opportunities are constantly expanding. You will be surrounded by some of the best talent in the industry, who will want to learn from you, too. Come join us!

The base salary range for this role is $188,000 to $275,000. The starting salary will be determined based on job-related knowledge, skills, experience, and market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility). 

What You’ll Do:
The Observability team builds and operates CoreWeave’s logging, tracing, and metrics platforms, along with the high-throughput data pipelines that power them. This team enables both internal engineers and customers to monitor, troubleshoot, and optimize AI workloads running on GPU-dense infrastructure at massive scale.

About the role:
As a Software Engineer on the Observability team, you will design, build, and maintain scalable systems that process and surface telemetry data across distributed environments. You’ll contribute production-quality code in languages like Go and Python, while improving system reliability through enhanced monitoring, alerting, and incident response practices. Day to day, you’ll collaborate with cross-functional engineering teams to implement observability best practices, support production systems, and help optimize performance across large-scale infrastructure. You will also participate in on-call rotations and contribute to continuous improvements based on real-world system behavior.

Who You Are:

• 2+ years of experience in Software Engineering, Site Reliability Engineering, DevOps, or a related field
• Proficiency in at least one programming or scripting language (e.g., Python, Go)
• Experience working with Kubernetes, containerization, and microservices architectures
• Experience participating in on-call rotations, including triaging and escalating production issues
• Hands-on experience using observability systems (metrics, logging, tracing) to debug distributed systems

Preferred:

• Experience operating observability platforms or databases (e.g., ClickHouse, Elastic, Loki, VictoriaMetrics, Prometheus, Thanos, OpenTelemetry, Grafana)
• Familiarity with infrastructure-as-code tools such as Terraform
• Experience with modern testing frameworks and deployment strategies (e.g., canary, blue-green)
• Experience with data streaming technologies (e.g., Kafka, Kafka Connect)
• Exposure to AI/ML infrastructure, including GPU-based systems, large-scale training/inference workloads, or MLOps tooling

Why CoreWeave?

At CoreWeave, we work hard, have fun, and move fast!  We’re in an exciting stage of hyper-growth that you will not want to miss out on. We’re not afraid of a little chaos, and we’re constantly learning. Our team cares deeply about how we build our product and how we work together, which is represented through our core values: 

• Be Curious at Your Core
• Act Like an Owner
• Empower Employees
• Deliver Best-in-Class Client Experiences
• Achieve More Together

We support and encourage an entrepreneurial outlook and independent thinking. We foster an environment that encourages collaboration and provides the opportunity to develop innovative solutions to complex problems. As we get set for take off, the growth opportunities within the organization are constantly expanding. You will be surrounded by some of the best talent in the industry, who will want to learn from you, too. Come join us! 

The base salary range for this role is $109,000 to $145,000. The starting salary will be determined based on job-related knowledge, skills, experience, and market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility).

What You’ll Do:

We are looking for a highly experienced and strategic Principal Engineer, Observability to lead the architecture, development, and operations of our Observability platform. In this role, you will define how customers monitor, troubleshoot, and operate their AI workloads at scale on CoreWeave.

You will work directly with customers and partner closely with engineering leaders across multiple teams to drive a unified Observability experience across CoreWeave products—spanning metrics, logs, traces, and customer-facing insights.

About the role:

• Lead the Observability strategy and roadmap, ensuring clear alignment with business goals, product direction, and performance/SLA objectives
• Design and implement low-latency, high-scale telemetry pipelines and data stores solutions that power observability across all CoreWeave products
• Build customer-facing experiences—dashboards, alerts, and workflows—that enable rapid troubleshooting and deep insight into AI workloads and platform health
• Drive reliability, durability, and self-healing across the Observability stack, owning key services in production and setting a high bar for operational excellence
• Shape customer experience for visibility by defining standards for metrics, SLOs, and dashboards that expose system performance and reliability in a clear, actionable way
• Analyze production telemetry to identify and prioritize improvements in performance, reliability, and efficiency across the platform
• Act as a trusted advisor to senior leadership, bringing perspective on observability trends and advocating for the right long-term technology investments
• Partner with engineering, infrastructure, and product teams to ensure Observability is deeply integrated into evolving architectures, APIs, and customer features
• Mentor and uplevel engineers across teams, promoting best practices in observability, distributed systems, and customer-focused product engineering

Who You Are:

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related field, or equivalent practical experience
• 15+ years of experience building and operating distributed systems at scale, with a strong track record of reliability-focused engineering
• Proven experience leading Observability platform engineering initiatives and delivering products that directly address customer needs
• Strong proficiency in one or more programming languages such as Go, Python, or Rust, and comfort working in large-scale production environments
• Deep understanding of distributed observability systems and telemetry pipelines (e.g., ClickHouse or similar technologies for telemetry at scale)
• Strong understanding of cloud-native infrastructure—Kubernetes, scalable architectures, automation, and modern CI/CD and deployment practices
• Excellent analytical and problem-solving skills, including the ability to synthesize ambiguous customer and system signals into clear problem statements and designs
• Exceptional communication and collaboration skills, able to articulate observability strategies, trade-offs, and best practices to both technical and non-technical stakeholders
• Prior experience building end-to-end Observability solutions (metrics, logs, traces, dashboards, alerting) in production environments

Preferred:

• Experience with modern AI platforms and workloads (e.g., large-scale training/ inference, GPU-based infrastructure, MLOps tooling) is a plus

Why CoreWeave?

At CoreWeave, we work hard, have fun, and move fast!  We’re in an exciting stage of hyper-growth that you will not want to miss out on. We’re not afraid of a little chaos, and we’re constantly learning. Our team cares deeply about how we build our product and how we work together, which is represented through our core values: 

• Be Curious at Your Core
• Act Like an Owner
• Empower Employees
• Deliver Best-in-Class Client Experiences
• Achieve More Together

We support and encourage an entrepreneurial outlook and independent thinking. We foster an environment that encourages collaboration and enables the development of innovative solutions to complex problems. As we get set for takeoff, the organization's growth opportunities are constantly expanding. You will be surrounded by some of the best talent in the industry, who will want to learn from you, too. Come join us!

The base salary range for this role is $206,000 to $303,000. The starting salary will be determined based on job-related knowledge, skills, experience, and market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility).

What You’ll Do
Our Solutions Architecture team at Weights & Biases is a unique hybrid organization, combining the deep technical skills of Site Reliability Engineering with the consultative expertise of Solutions Architecture. We focus on ensuring customers can successfully deploy and operate W&B across cloud and on-prem environments while delivering a best-in-class experience that accelerates ML adoption at scale.

About the Role
As a Solutions Architect, you will be responsible for managing complex customer deployments across AWS, GCP, Azure, and on-prem environments. You’ll partner directly with customer engineering teams to provision and monitor services, debug and resolve infrastructure issues, and ensure performance and scalability using SRE best practices. This role blends hands-on technical problem-solving with customer-facing engagement, including technical discussions, demos, workshops, and enablement content creation. You’ll work closely with Sales Engineering, Field Engineering, Support, and Product to drive adoption and influence our product roadmap based on customer feedback.

Who You Are

• Proven track record of systematically diagnosing and resolving infrastructure issues
• Experience in a customer-facing technical role
• Expertise in Docker, Kubernetes, Helm charts, networking, and cloud-managed services (e.g., MySQL, Object Stores)
• Strong fundamentals in Infrastructure as Code (IaC), preferably Terraform
• Proficiency with at least one cloud platform (AWS, GCP, Azure); experience across multiple platforms is a plus
• Strong Linux/Unix command line experience
• Basic proficiency in Python and familiarity with ML workflows or tools

Preferred

• Deep proficiency in Kubernetes design patterns, including Operators
• Familiarity with data engineering and MLOps tooling
• Experience as an educator or facilitator for technical training sessions, workshops, or demos
• SaaS, web service, or distributed systems operations experience

Wondering if you’re a good fit?
We believe in investing in our people, and value candidates who can bring their own diversified experiences to our teams – even if you aren't a 100% skill or experience match. Here are a few qualities we’ve found compatible with our team. If some of this describes you, we’d love to talk.

• You love diving into infrastructure problems and solving them systematically
• You’re curious about how to scale complex ML systems in production environments
• You’re an expert in building and running containerized, distributed systems

Why Us?
We work hard, have fun, and move fast! We’re in an exciting stage of hyper-growth that you will not want to miss out on. We’re not afraid of a little chaos, and we’re constantly learning. Our team cares deeply about how we build our product and how we work together, which is represented through our core values:

• Be Curious at Your Core
• Act Like an Owner
• Empower Employees
• Deliver Best-in-Class Client Experiences
• Achieve More Together

We support and encourage an entrepreneurial outlook and independent thinking. We foster an environment that encourages collaboration and provides the opportunity to develop innovative solutions to complex problems. As we get set for takeoff, the growth opportunities within the organization are constantly expanding. You will be surrounded by some of the best talent in the industry, who will want to learn from you, too. Come join us!

The base salary ranges for this role is $180,000 to $200,000. The starting salary will be determined based on job-related knowledge, skills, experience, and market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility).

Senior Manager, Data Science

Portfolio Management and Loss Mitigation

The Role: 

SoFi is seeking an inspirational and deeply experienced Senior Manager to lead the Portfolio Management and Loss Mitigation function in our Risk Data Science team. Reporting to the Head of Risk Data Science, this role will lead the development, deployment, and governance of portfolio management and loss mitigation models for all credit products.

The ideal candidate is a hands-on leader who can transition the team from traditional modeling to next-generation machine learning platforms, leveraging emerging data sources (e.g., cash flow, alternative bureaus) to significantly improve portfolio performance, reduce losses, and ensure rigorous adherence to Model Risk Management (MRM) standards. This role requires exceptional organizational leadership, an ability to influence stakeholders, and proven success in delivering complex models into a regulated production environment.

What You'll Do: 

• Portfolio Management and Loss Mitigation Excellence: Directly oversee the development and deployment of Next Generation models designed to manage and mitigate portfolio losses while maintaining loss guardrails.
• Drive Next-Generation Capabilities: Incorporate industry trends and advanced techniques (NLP, Graph Mining, LLMs, Deep Learning) to solve complex, high-impact risk problems where established principles may not fully apply.
• Alternative Data Strategy: Spearhead the evaluation and integration of alternative data sources (Macro-Economic variables, tri-bureau, LexisNexis, cash flow data) to enhance predictive power across all credit products.
• Lead the current team of high-performing Staff and Senior Data Scientists. Recruit, mentor, and foster talent through deliberate interactions, succession planning, and creating a high-accountability, low-ego culture.
• Model Risk Management (MRM): Act as the primary owner for all models in the portfolio, ensuring robust documentation, monitoring, and successfully navigating the 2nd Line of Defense (2LOD) review and approval process (SR 11-7 familiarity is mandatory).
• Stakeholder Alignment: Interact and negotiate with senior management and external stakeholders to reconcile competing views and drive critical, high-impact business decisions.
• Automation and Efficiency: Lead efforts to automate model monitoring and governance processes to create scalable and auditable infrastructure.

What You'll Need:

• Experience: 8+ years of progressive experience in credit risk, modeling, and data science within a regulated financial institution (FinTech, Bank, or similar), with at least 3 years in a people management role, managing technical staff.
• Education: Master’s or Ph.D. degree in a quantitative field (Statistics, Computer Science, Engineering, Operations Research, etc.).
• Technical Acumen: Deep expertise in advanced statistical and machine learning modeling techniques (e.g., Gradient Boosting, Deep Learning, Causal Inference).
• Regulatory Knowledge: Detailed working knowledge of model risk management standards (e.g., SR 11-7) and the ability to operate within a highly regulated environment.
• Tools & Platforms: Expert-level proficiency in Python (PySpark, scikit-learn, TensorFlow/PyTorch) and SQL/data warehouse technologies (e.g., Snowflake, Hive). Familiarity with modern MLOps platforms and cloud computing (AWS).
• 3+ years of progressive people management experience is required, including the ability to recruit, mentor, and foster talent within a team of high-performing Staff and Senior Data Scientists.
• Communication: Ability to distill highly complex analytical concepts into clear, concise, and compelling narratives for non-technical leadership.

DRW is a diversified trading firm with over 3 decades of experience bringing sophisticated technology and exceptional people together to operate in markets around the world. We value autonomy and the ability to quickly pivot to capture opportunities, so we operate using our own capital and trading at our own risk.

Headquartered in Chicago with offices throughout the U.S., Canada, Europe, and Asia, we trade a variety of asset classes including Fixed Income, ETFs, Equities, FX, Commodities and Energy across all major global markets. We have also leveraged our expertise and technology to expand into three non-traditional strategies: real estate, venture capital and cryptoassets.

We operate with respect, curiosity and open minds. The people who thrive here share our belief that it’s not just what we do that matters–it's how we do it. DRW is a place of high expectations, integrity, innovation and a willingness to challenge consensus.

As an AI Engineer at DRW, you will be an integral member of a collaborative multi-asset trading team. You’ll work on high-impact machine learning (ML) and artificial intelligence (AI) initiatives that are central to our business strategy. In this role, you will build critical infrastructure and tooling that leverages AI and ML technology to enhance productivity, efficiency, and discover innovative paths to challenging business and technology problems. Surrounded by cutting-edge technology and experienced trading teams, you will have the opportunity to tackle fascinating new projects while becoming a subject-matter expert in a critical and innovative role.

Key Responsibilities:

• Drive end-to-end development of AI infrastructure and AI driven applications: from initial proof-of-concept to production deployment and ongoing maintenance.
• Collaborate with technologists, traders, quantitative researchers, and data scientists to identify high-impact opportunities for integrating AI and machine learning into technology and business use cases.
• Implement automated systems for continuous training, validation, and monitoring of models, minimizing downtime and ensuring reliability.
• Provide technical leadership in selecting, integrating, and optimizing AI and ML frameworks, libraries, and tools across diverse hardware and software environments.
• Create and maintain feature pipelines, feature stores, and model stores.
• Develop frameworks to enable scalable, reproducible research.
• Proactively troubleshoot performance bottlenecks, conduct root-cause analyses, and implement solutions to optimize GPU or CPU resource usage.

Qualifications:

• Bachelor’s or advanced degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
• 3+ years of experience working with machine learning and artificial intelligence technology.
• Strong understanding of core machine learning and artificial intelligence concepts.
• Excellent programming skills in Python.
• Demonstrated experience in building, validating, deploying, monitoring, and updating production ML and AI models.
• Hands-on experience with MLOps and AIOps infrastructure and tooling.
• Proficient in problem-solving and analytical reasoning.
• Exceptional communication and collaboration skills.
• Experience with ML frameworks such as TensorFlow, PyTorch, TensorRT, or ONNX.
• Experience with Large Language Models, including RAG and fine-tuning techniques.
• Familiarity with compute infrastructure necessary to support operating AI and ML technology.

The annual base salary range for this position is $175,000 to $300,000, depending on the candidate’s experience, qualifications, and relevant skill set. The position is also eligible for an annual discretionary bonus.  In addition, DRW offers a comprehensive suite of employee benefits including group medical, pharmacy, dental and vision insurance, 401k (with discretionary employer match), short and long-term disability, life and AD&D insurance, health savings accounts, and flexible spending accounts.

For more information about DRW's processing activities and our use of job applicants' data, please view our Privacy Notice at https://drw.com/privacy-notice.

California residents, please review the California Privacy Notice for information about certain legal rights at https://drw.com/california-privacy-notice.

#LI-BL1

DRW is a diversified trading firm with over 3 decades of experience bringing sophisticated technology and exceptional people together to operate in markets around the world. We value autonomy and the ability to quickly pivot to capture opportunities, so we operate using our own capital and trading at our own risk.

Headquartered in Chicago with offices throughout the U.S., Canada, Europe, and Asia, we trade a variety of asset classes including Fixed Income, ETFs, Equities, FX, Commodities and Energy across all major global markets. We have also leveraged our expertise and technology to expand into three non-traditional strategies: real estate, venture capital and cryptoassets.

We operate with respect, curiosity and open minds. The people who thrive here share our belief that it’s not just what we do that matters–it's how we do it. DRW is a place of high expectations, integrity, innovation and a willingness to challenge consensus.

As an AI Engineer at DRW, you will be an integral member of a collaborative multi-asset trading team. You’ll work on high-impact machine learning (ML) and artificial intelligence (AI) initiatives that are central to our business strategy. In this role, you will build critical infrastructure and tooling that leverages AI and ML technology to enhance productivity, efficiency, and discover innovative paths to challenging business and technology problems. Surrounded by cutting-edge technology and experienced trading teams, you will have the opportunity to tackle fascinating new projects while becoming a subject-matter expert in a critical and innovative role.

Key Responsibilities:

• Drive end-to-end development of AI infrastructure and AI driven applications: from initial proof-of-concept to production deployment and ongoing maintenance.
• Collaborate with technologists, traders, quantitative researchers, and data scientists to identify high-impact opportunities for integrating AI and machine learning into technology and business use cases.
• Implement automated systems for continuous training, validation, and monitoring of models, minimizing downtime and ensuring reliability.
• Provide technical leadership in selecting, integrating, and optimizing AI and ML frameworks, libraries, and tools across diverse hardware and software environments.
• Create and maintain feature pipelines, feature stores, and model stores.
• Develop frameworks to enable scalable, reproducible research.
• Proactively troubleshoot performance bottlenecks, conduct root-cause analyses, and implement solutions to optimize GPU or CPU resource usage.

Qualifications:

• Bachelor’s or advanced degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
• 3+ years of experience working with machine learning and artificial intelligence technology.
• Strong understanding of core machine learning and artificial intelligence concepts.
• Excellent programming skills in Python.
• Demonstrated experience in building, validating, deploying, monitoring, and updating production ML and AI models.
• Hands-on experience with MLOps and AIOps infrastructure and tooling.
• Proficient in problem-solving and analytical reasoning.
• Exceptional communication and collaboration skills.
• Experience with ML frameworks such as TensorFlow, PyTorch, TensorRT, or ONNX.
• Experience with Large Language Models, including RAG and fine-tuning techniques.
• Familiarity with compute infrastructure necessary to support operating AI and ML technology.

The annual base salary range for this position is $175,000 to $300,000, depending on the candidate’s experience, qualifications, and relevant skill set. The position is also eligible for an annual discretionary bonus.  In addition, DRW offers a comprehensive suite of employee benefits including group medical, pharmacy, dental and vision insurance, 401k (with discretionary employer match), short and long-term disability, life and AD&D insurance, health savings accounts, and flexible spending accounts.

For more information about DRW's processing activities and our use of job applicants' data, please view our Privacy Notice at https://drw.com/privacy-notice.

California residents, please review the California Privacy Notice for information about certain legal rights at https://drw.com/california-privacy-notice.

#LI-BL1

Generative AI Architect
Location: New York, NY & Orlando, FL | Practice Area: Technology & Engineering | Type: Permanent

Shape the architecture of enterprise-scale generative AI solutions at the forefront of innovation

The Role
As part of Capco’s Technology Delivery Team, you will apply deep expertise in architecture, AI, and modern engineering practices to design and deliver next-generation solutions. As a Generative AI Architect, you will lead the definition and implementation of scalable, secure, and enterprise-grade AI systems, guiding projects from concept through to production while aligning technology with strategic business goals.

What You’ll Do

• Define and deliver enterprise-wide architecture for generative AI and agentic systems aligned to business objectives
• Design scalable, modular solution patterns and integrate foundation models into enterprise environments
• Architect and implement agentic AI systems, including workflows, orchestration, and system integrations
• Establish robust cloud-native infrastructure and MLOps pipelines for deployment, monitoring, and lifecycle management
• Collaborate across teams, mentor colleagues, and contribute to innovation and thought leadership in AI

What We’re Looking For

• Bachelor’s or Master’s degree in Computer Science, AI/ML, or a related field
• 10+ years of experience in software architecture, including hands-on work with AI/ML systems
• Strong expertise in generative AI, foundation models, and agentic AI frameworks
• Proficiency in Python and experience with leading AI/ML frameworks and cloud platforms (AWS, Azure, or GCP)
• Proven ability to communicate complex technical concepts and influence technical decision-making
• WIllingness to work in our office 3 days/week

Bonus Points For

• Experience with prompt engineering and advanced RAG-based architectures
• Familiarity with vector databases and unstructured data processing techniques
• Experience implementing MLOps frameworks and production-grade AI systems
• Exposure to containerized and distributed systems (e.g., Kubernetes)
• Experience contributing to knowledge sharing, workshops, or external thought leadership

Why Join Capco

• Deliver high-impact technology solutions for Tier 1 financial institutions
• Work in a collaborative, flat, and entrepreneurial consulting culture
• Access continuous learning, training, and industry certifications
• Be part of a team shaping the future of digital financial services
• Help shape the future of digital transformation across FS & Energy

We offer a competitive, people-first benefits package designed to support every aspect of your life:

• Medical, dental, and vision coverage
• 401(k) with employer match
• Paid parental leave and dependent care support
• Tuition and wellness reimbursement programs
• Commuter benefits and access to employee discount platforms

Use of Artificial Intelligence in Talent Acquisition
At Capco, we use artificial intelligence (AI) tools to support and enhance several parts of talent acquisition. This includes using AI-enabled features within LinkedIn to help source potential candidates, speeding up routine recruitment communications such as emails and creating compelling and brand-aligned job postings that accurately reflect role requirements, and AI-scheduling applications to improve the efficiency of interview coordination.

AI is used as a support tool only. All hiring decisions are made by talent acquisition and hiring teams.

Inclusion at Capco
We’re committed to making our recruitment process accessible and straightforward for everyone. If you need any adjustments at any stage, just let us know – we’ll be happy to help. We value each person’s unique perspective and contribution. At Capco, we believe that being yourself is your greatest strength. Our #BeYourselfAtWork culture encourages individuality and collaboration – a mindset that shapes how we work with clients and each other every day.

We have been informed of several recruitment scams targeting the public. We strongly advise you to verify identities before engaging in recruitment related communication. All official Capco communication will be conducted via a Capco recruiter.

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Senior GenAI Engineer
Location: New York, NY & Dallas, TX | Practice Area: Technology & Engineering | Type: Permanent

Design and deliver next-generation AI solutions shaping the future of financial services

The Role
As part of Capco’s Technology Delivery Team, you will bring hands-on experience in agile development and engineering best practices to deliver high-impact AI solutions. As a Senior GenAI Engineer, you will play a key role across the full AI lifecycle—from research and prototyping through to production deployment—helping deliver transformative, enterprise-grade solutions for our clients.

What You’ll Do

• Build applications leveraging foundation models and advanced GenAI techniques (Agentic RAG, KG-RAG)
• Develop autonomous agents using modern agentic frameworks
• Implement MCP integrations and Python-based full-stack solutions
• Design scalable REST APIs and deploy AI workloads across cloud platforms
• Collaborate across teams, mentor colleagues, and contribute to innovation in embeddings and knowledge systems

What We’re Looking For

• Bachelor’s degree in Computer Science, AI/ML, or a related field
• 5+ years of experience in AI/ML-focused software engineering
• Hands-on production experience with LLMs and agentic systems
• Strong expertise in Python and Model Context Protocol (MCP)
• Experience with cloud platforms (AWS, Azure, or GCP) and modern development practices
• Willingness to work on-site in our office 5 days/week

Bonus Points For

• Experience with embeddings at scale and knowledge graph implementation
• Familiarity with ontology extraction and advanced RAG architectures
• Full-stack development experience across backend and frontend technologies
• Exposure to LLMOps/MLOps tools for CI/CD and monitoring
• Experience mentoring or supporting team development initiatives

Why Join Capco

• Deliver high-impact technology solutions for Tier 1 financial institutions
• Work in a collaborative, flat, and entrepreneurial consulting culture
• Access continuous learning, training, and industry certifications
• Be part of a team shaping the future of digital financial services
• Help shape the future of digital transformation across FS & Energy

We offer a competitive, people-first benefits package designed to support every aspect of your life:

• Medical, dental, and vision coverage
• 401(k) with employer match
• Paid parental leave and dependent care support
• Tuition and wellness reimbursement programs
• Commuter benefits and access to employee discount platforms

Use of Artificial Intelligence in Talent Acquisition
At Capco, we use artificial intelligence (AI) tools to support and enhance several parts of talent acquisition. This includes using AI-enabled features within LinkedIn to help source potential candidates, speeding up routine recruitment communications such as emails and creating compelling and brand-aligned job postings that accurately reflect role requirements, and AI-scheduling applications to improve the efficiency of interview coordination.

AI is used as a support tool only. All hiring decisions are made by talent acquisition and hiring teams.

Inclusion at Capco
We’re committed to making our recruitment process accessible and straightforward for everyone. If you need any adjustments at any stage, just let us know – we’ll be happy to help. We value each person’s unique perspective and contribution. At Capco, we believe that being yourself is your greatest strength. Our #BeYourselfAtWork culture encourages individuality and collaboration – a mindset that shapes how we work with clients and each other every day.

We have been informed of several recruitment scams targeting the public. We strongly advise you to verify identities before engaging in recruitment related communication. All official Capco communication will be conducted via a Capco recruiter.

#LI-MB1

#LI-HYBRID

Senior GenAI Engineer
Location: New York, NY & Dallas, TX | Practice Area: Technology & Engineering | Type: Permanent

Design and deliver next-generation AI solutions shaping the future of financial services

The Role
As part of Capco’s Technology Delivery Team, you will bring hands-on experience in agile development and engineering best practices to deliver high-impact AI solutions. As a Senior GenAI Engineer, you will play a key role across the full AI lifecycle—from research and prototyping through to production deployment—helping deliver transformative, enterprise-grade solutions for our clients.

What You’ll Do

• Build applications leveraging foundation models and advanced GenAI techniques (Agentic RAG, KG-RAG)
• Develop autonomous agents using modern agentic frameworks
• Implement MCP integrations and Python-based full-stack solutions
• Design scalable REST APIs and deploy AI workloads across cloud platforms
• Collaborate across teams, mentor colleagues, and contribute to innovation in embeddings and knowledge systems

What We’re Looking For

• Bachelor’s degree in Computer Science, AI/ML, or a related field
• 5+ years of experience in AI/ML-focused software engineering
• Hands-on production experience with LLMs and agentic systems
• Strong expertise in Python and Model Context Protocol (MCP)
• Experience with cloud platforms (AWS, Azure, or GCP) and modern development practices
• Willingness to work on-site in our office 3 days/week

Bonus Points For

• Experience with embeddings at scale and knowledge graph implementation
• Familiarity with ontology extraction and advanced RAG architectures
• Full-stack development experience across backend and frontend technologies
• Exposure to LLMOps/MLOps tools for CI/CD and monitoring
• Experience mentoring or supporting team development initiatives

Why Join Capco

• Deliver high-impact technology solutions for Tier 1 financial institutions
• Work in a collaborative, flat, and entrepreneurial consulting culture
• Access continuous learning, training, and industry certifications
• Be part of a team shaping the future of digital financial services
• Help shape the future of digital transformation across FS & Energy

We offer a competitive, people-first benefits package designed to support every aspect of your life:

• Medical, dental, and vision coverage
• 401(k) with employer match
• Paid parental leave and dependent care support
• Tuition and wellness reimbursement programs
• Commuter benefits and access to employee discount platforms

Use of Artificial Intelligence in Talent Acquisition
At Capco, we use artificial intelligence (AI) tools to support and enhance several parts of talent acquisition. This includes using AI-enabled features within LinkedIn to help source potential candidates, speeding up routine recruitment communications such as emails and creating compelling and brand-aligned job postings that accurately reflect role requirements, and AI-scheduling applications to improve the efficiency of interview coordination.

AI is used as a support tool only. All hiring decisions are made by talent acquisition and hiring teams.

Inclusion at Capco
We’re committed to making our recruitment process accessible and straightforward for everyone. If you need any adjustments at any stage, just let us know – we’ll be happy to help. We value each person’s unique perspective and contribution. At Capco, we believe that being yourself is your greatest strength. Our #BeYourselfAtWork culture encourages individuality and collaboration – a mindset that shapes how we work with clients and each other every day.

We have been informed of several recruitment scams targeting the public. We strongly advise you to verify identities before engaging in recruitment related communication. All official Capco communication will be conducted via a Capco recruiter.

#LI-MB1

#LI-HYBRID

Position Summary

We are seeking a skilled Machine Learning Engineer with approximately three years of hands-on experience designing, deploying, and maintaining production-grade machine learning systems. In this role, you will collaborate closely with data scientists, software engineers, and product teams to translate research models into reliable, scalable, and high-impact applications. You will be deeply involved in the end-to-end ML lifecycle—from data ingestion and feature engineering to deployment, monitoring, and continuous improvement—playing a critical part in shaping our machine learning platform and capabilities.

Primary Responsibilities

• Develop, deploy, and optimize machine learning models for real-world business use cases and client-facing applications.
• Partner with data scientists to operationalize predictive models and ensure scalable, maintainable, and performant production deployments.
• Design and implement data pipelines and workflows that support training, inference, and model lifecycle management.
• Work with large, complex datasets to ensure data quality, reproducibility, and reliable version control across ML workflows.
• Implement model monitoring, logging, and alerting strategies to track performance, detect drift, and support retraining cycles.
• Leverage cloud platforms (AWS, Azure, GCP) to build scalable ML solutions using managed services and infrastructure-as-code practices.
• Write clean, modular, and well-documented code aligned with MLOps and software engineering best practices.
• Stay current on emerging ML tooling, frameworks, and industry best practices to continuously enhance our platform and capabilities.

Qualifications

• Master’s degree in Computer Science, Data Science, Engineering, or a related technical field.
• 6+ years of experience in machine learning engineering, applied ML, or related software engineering roles.
• Strong proficiency in Python and experience with modern ML frameworks such as TensorFlow, PyTorch, or scikit-learn.
• Experience with distributed data processing and compute frameworks (e.g., Pandas, Spark, Dask).
• Hands-on experience with containerization and orchestration technologies such as Docker and Kubernetes.
• Familiarity with CI/CD pipelines, testing automation, and version control using Git.
• Experience working with cloud-based ML platforms or services (e.g., SageMaker, Vertex AI, Databricks, or Snowflake ML) is preferred.
• Strong understanding of model evaluation, feature engineering, and performance optimization in production contexts.
• Excellent analytical, communication, and collaboration skills, with the ability to work effectively in cross-functional teams.

This is an exempt position. The annualized base pay range for this role is expected to be between $140,000-$180,000.  Actual base pay could vary based on factors including but not limited to experience, subject matter expertise, geographic location where work will be performed, and the applicant's skill set.  The base pay is just one component of the total compensation package for employees.  Other rewards may include an annual cash bonus and a comprehensive benefits package, including but not limited to medical, dental, vision, life and 401(k). Please note that the job title is subject to change based on the selected candidate’s experience and education.

Software Engineer, Machine Learning (MLOps & Data)

A Career with Point72’s Surveillance Team

On the Knowledge Graph Intelligence team, you’ll work alongside product managers, engineers, and data scientists to build the next generation of intelligent systems through graph technology. We’re a team of experts who experiment and work to discover new ways to harness open-source solutions, modern cloud architectures, and sophisticated Artificial Intelligence (AI) solutions, while embracing enterprise agile methodologies. Our commitment to building and innovating in the AI space provides the framework intended to drive smarter decision-making and enhance how we build and operate our platforms and applications.

What you’ll do

In this data-heavy role, you will design and build mission-critical infrastructure that powers our machine learning lifecycle, from large-scale data processing and feature engineering to model training, real-time deployment, and monitoring. Specifically, you will:

• Architect and implement the full lifecycle of ML models, from data ingestion to production inference, contributing to the design of our next-generation, event-driven architecture, using technologies like gRPC, Kafka, and high-performance API frameworks, like FastAPI, Spring WebFlux, and Axum.
• Engineer and automate robust, large-scale data processing pipelines (ETL/ELT) using tools like Spark, dbt, and workflow orchestrators, and lead the design and implementation of our Feature Store strategy.
• Own the MLOps framework for model training, versioning, and deployment, including CI/CD pipelines, automated workflows, and experiment tracking and evaluation tooling.
• Implement sophisticated deployment strategies, including canary, blue-green, shadow, and A/B testing, to ensure safe, zero-downtime releases, and optimize inference performance for LLMs and other large models.
• Leverage cutting-edge tools and techniques like quantization and compilation to maximize throughput and minimize latency.
• Collaborate with data scientists to develop models and optimize model performance for low-latency serving using techniques like Python performance tuning.
• Define, provision, and manage our cloud infrastructure using Terraform, working hands-on with a wide array of cloud services across compute, storage, and machine learning platforms.

What’s REQUIRED

• 5+ years of experience in a software, data, or ML engineering role.
• Strong proficiency in SQL.
• Experience building and orchestrating data pipelines using tools such as Spark, dbt, and Dagster/Airflow, as well as data warehouses like Snowflake, Redshift, BigQuery.
• Understanding of infrastructure as code, including experience with Terraform.
• Proficiency with containerization and orchestration including Docker and Kubernetes.
• Hands-on experience with CI/CD tools and ML lifecycle tools, including Jenkins, MLflow, Kubeflow, and W&B.
• Experience with AWS and its core services including S3, EC2, Lambda, RDS, and EMR, and practical experience with Boto3 and AWS ML services SageMaker and Bedrock.
• Understanding of modern ML models and ability to discuss the performance characteristics and engineering trade-offs that influence deployment decisions.
• Experience with systems incorporating Graph Neural Networks (GNNs), recommendation systems, anomaly detection, and complex time-series models.
• Commitment to the highest ethical standards.

We take care of our people

We invest in our people, their careers, their health, and their well-being. When you work here, we provide:

• Fully-paid health care benefits
• Generous parental and family leave policies
• Volunteer opportunities
• Support for employee-led affinity groups representing women, people of color and the LGBT+ community
• Mental and physical wellness programs
• Tuition assistance
• A 401(k) savings program with an employer match and more

About point72

Point72 is a leading global alternative investment firm led by Steven A. Cohen. Building on more than 30 years of investing experience, Point72 seeks to deliver superior returns for its investors through fundamental and systematic investing strategies across asset classes and geographies. We aim to attract and retain the industry’s brightest talent by cultivating an investor-led culture and committing to our people’s long-term growth. For more information, visit https://point72.com/.

The annual base salary range for this role is $175,000-$250,000 (USD) , which does not include discretionary bonus compensation or our comprehensive benefits package. Actual compensation offered to the successful candidate may vary from posted hiring range based upon geographic location, work experience, education, and/or skill level, among other things.

ABOUT THE ROLE 

This role sits within Anaplan's IT Customer Experience organization and is focused on transforming how Anaplan employees work. You will design, build, and scale AI tools, agents, and capabilities that drive productivity, smarter decision-making, and operational efficiency across our global workforce. 

Anaplan is building its Enterprise AI Center of Excellence (COE) — a one-stop shop for all internal AI at Anaplan — and is looking for an AI Principal Architect to lead it from a technical standpoint. This is a senior, newly created role at the intersection of hands-on enterprise AI engineering, internal system architecture, technical leadership, and workforce enablement. 

The AI Principal Architect will own the technical vision for Anaplan's internal AI — designing the reference architecture for LLMs, agentic AI, and Gemini Enterprise; setting engineering standards; evaluating emerging platforms; and ensuring every employee-facing initiative is built with rigor, scalability, and responsible design. 

This role carries a hybrid profile: deep individual technical contribution combined with team leadership influence. You will mentor engineers, guide architectural decisions, serve as the primary technical escalation point, and contribute a forward-looking perspective to the 2–3 year AI technology roadmap — without necessarily holding formal management authority over all contributors. 

You will report directly to the Director, IT Customer Experience, who co-leads the Enterprise AI COE alongside the Financial Flexibility Lead and a GTM Champion as a 3-in-a-box. You will collaborate closely with Technical/Engineering teams (dotted-line, for agent development and delivery) and the Transformation & Financial Flexibility team (to identify AI-driven cost-saving opportunities). 

KEY RESPONSIBILITIES 

Reference Architecture & Enterprise AI Engineering 

• Own and publish the reference architecture for Anaplan's internal AI — covering LLMs, agentic AI frameworks, Gemini Enterprise integrations, and data pipelines. 

• Architect end-to-end internal enterprise AI solutions designed for the scale, security, and reliability requirements of a global workforce. 

• Set and enforce AI engineering standards, coding practices, code review processes, and technical guardrails across all internal AI projects. 

• Lead hands-on technical design reviews and guide implementation decisions from intake through production deployment. 

• Evaluate, select, and onboard new enterprise AI platforms and tools — providing technical recommendations grounded in internal workforce needs. 

• Design the LLM abstraction layer and multi-model strategy to optimize cost, performance, and flexibility. 

• Own technical debt management, system observability, and scalability planning for Anaplan's internal AI systems. 

AI Platform Governance & Enablement 

• Define and maintain technical evaluation criteria for the AI intake process — assessing feasibility, build complexity, security risk, and integration requirements for employee-facing tools. 

• Ensure all internal AI initiatives pass engineering, security, compliance, and responsible AI review before development begins. 

• Maintain enterprise AI technical policies, engineering standards, and responsible AI guardrails in partnership with Security and Compliance. 

• Translate approved internal use cases into clear technical specifications and hand off to engineering teams with full architectural context. 

• Define enterprise AI data layer and integration architecture across core systems (Salesforce, ServiceNow, Gainsight, Workday). 

• Integrate with ServiceNow AI Control Tower to track all AI use cases, agents, and realized business value. 

• Implement human-in-the-loop, explainability, and auditability frameworks for all decision-impacting AI systems. 

• Partner with Finance to define and measure AI-driven ROI, ensuring alignment with enterprise value targets. 

• Establish full lifecycle management for AI agents, including monitoring, feedback loops, and continuous improvement. 

Technical Team Leadership 

• Provide technical direction, coaching, and mentorship to engineers across the AI Governance & Enablement and AI Operations & Insights teams. 

• Serve as the primary technical escalation point for complex internal AI delivery challenges and architecture decisions. 

• Partner with Technical/Engineering teams (dotted-line) on agent architecture, build quality, and delivery timelines. 

• Help define the technical bar for AI team hiring and contribute to onboarding and skills development programs. 

• Foster a culture of engineering rigor, experimentation, and continuous improvement across the internal AI function. 

AI Operations & Workforce Adoption 

• Oversee the technical deployment and operationalization of AI agents and solutions, ensuring production readiness and stability for Anaplan employees. 

• Drive Gemini Enterprise adoption and lead AI Friday sessions with genuine technical depth. 

• Build and maintain engineering dashboards tracking internal AI system performance, adoption rates, and ROI metrics. 

• Translate engineering decisions into executive-ready business value narratives for the Director, CIO, and Finance audiences. 

Innovation, R&D & Emerging Technology 

• Research and prototype emerging AI/ML techniques, large language models, agent frameworks, and enterprise tooling to identify internal productivity and cost-saving opportunities. 

• Design and lead proof-of-concept pilots that validate novel internal use cases before committing to full engineering investment. 

• Build and maintain the AI innovation pipeline — a living backlog of high-potential experiments. 

• Contribute the technical perspective on where internal AI capabilities should be invested over the next 2–3 years, collaborating with the Director on the roadmap. 

Cross-Functional Technical Partnership 

• Work hand in hand with the Transformation & Financial Flexibility team to identify and technically validate AI-driven cost-saving opportunities in Anaplan's internal operations. 

• Act as technical advisor and AI subject matter expert to business units identifying internal AI use cases. 

• Support the 3-in-a-box COE co-leadership (IT, Finance, GTM) with technical analysis, architecture reviews, and feasibility assessments. 

• Build trusted technical relationships across Engineering, Security, Compliance, Legal, and HR. 

REQUIRED QUALIFICATIONS 

• 7+ years of software or AI/ML engineering experience, with at least 3 years in a technical lead, staff engineer, or principal-level role — ideally with exposure to enterprise IT or internal tooling environments. 

• Deep hands-on expertise across the AI/ML stack: LLMs, retrieval-augmented generation (RAG), agent frameworks, or MLOps pipelines — with experience in enterprise or workforce deployment contexts. 

• Proven experience architecting and deploying enterprise-scale internal AI systems from design through production, including reliability, observability, and performance engineering. 

• Proficiency in Python and enterprise cloud AI platforms (GCP Vertex AI, Azure AI, or AWS SageMaker) and AI orchestration tooling. 

• Hands-on experience with Google Workspace AI and Gemini Enterprise, or equivalent enterprise AI platforms (e.g., Microsoft Copilot, OpenAI Enterprise). 

• Demonstrated ability to provide technical leadership, mentorship, and architectural guidance to engineering teams without formal management authority. 

• Ability to manage competing priorities, dependencies, and delivery timelines across multiple workstreams. 

• Strong business acumen and communication skills — able to translate complex enterprise AI/ML concepts into business value narratives for C-level audiences. 

• Experience in enterprise SaaS environments with complex stakeholder landscapes, security requirements, and compliance obligations. 

PREFERRED QUALIFICATIONS 

• Experience with AI governance frameworks, responsible AI principles, or enterprise AI policy and risk management for internal deployments. 

• Familiarity with ServiceNow or similar ITSM platforms and integration patterns. 

• Background in enterprise change management, internal AI enablement, or large-scale digital transformation programs. 

• Experience working with cross-functional finance or business transformation teams on AI-driven cost optimization. 

• Prior experience in an internal IT or enterprise technology function at a high-growth SaaS company. 
• Enterprise technology function at a high-growth SaaS company. 

About the role

Hex is an AI-powered platform for modern Data Science and Data Analytics workflows. AI Research Engineers at Hex partner with product teams to build industry-leading AI experiences such as the Notebook Agent. In the process, AI Research Engineers will run experiments, fine-tune models, deploy AI infrastructure, and build and maintain experimentation tooling. 

As an AI Research Engineer partnered with Hex's product teams, your focus will be driving Hex's context engine and pushing forward the capabilities of our Notebook Agent. Built for people doing the most complex and impactful data work in an organization, the Notebook Agent is a highly technical data copilot that writes SQL and python, builds beautiful reports and visualizations, and collaborates with the analyst on new avenues of inquiry. Whether its pushing the frontiers of SQL generation quality or using LLMs to evaluate agent threads, your job will be to enable the data teams that work in Hex to build out highly accurate and curated data experiences for their stakeholders, unlocking the power of data driven decisions for the rest of the business.

If you’re a strong builder excited to bring these capabilities to thousands of users, come do it on the best Data Science platform around, with the best user context.

We are looking for a senior engineer (from an AI Eng, SWE or MLE background) eager to rapidly expand our capabilities in several greenfield applications. As one of our team’s early hires, you will work on a variety of initiatives including:

• Building features and experiences from 0 to 1
• Partnering on determining the architecture and stack for our AI-enabled capabilities
• Shipping product experiences that fundamentally change the way that Data Scientists and Analysts operate
• Working at the cutting edge of production AI applications

This type of ownership will provide incredible opportunities for personal and professional growth. You will have ample technical and team leadership opportunities, depending on your interests.

We aim to deliver AI capabilities that radically accelerate and augment the data science workflow – we’ve got an exciting roadmap and are willing to share more details during the process. We’re particularly interested in candidates who bring inspired excitement about what is possible in this space.

We'd love to hear from you if you have:

• Experience getting AI/ML capabilities into production and serving real users – we’re not currently looking for new grad folks.
• A lot of enthusiasm for applications of AI to real business problems.
• Understanding of core MLOps/SW Architecture concepts for modern ML-based applications.
  • Ideal candidates are strong on Infra aspects of MLOps as well.
• You’re comfortable working in both Python & JS/TS – it’s okay if you’re only strong in one, but openness to both is important.
• Experimentalist mindset – understanding that quick iteration, and discerning critical thinking, can yield rapid progress on new challenges.
• Interest in the data space, and a love of shipping great products and building tools that empower end users to do more.
• Experience maintaining a high quality bar for design, correctness, and testing.

Our stack

Our product is a web-based notebook and app authoring platform. Our frontend is built with Typescript and React, using a combination of Apollo GraphQL and Redux for managing application state and data. On the backend, we also use Typescript to power an Express/Apollo GraphQL server that interacts with Postgres, Redis, and Kubernetes to manage our database and Python kernels. Our backend is tightly integrated with our infrastructure and CI/CD, where we use a combination of Terraform, Helm, and AWS to deploy and maintain our stack.

This is a full-time role based out of either our San Francisco or New York office. Employees are encouraged to come into the office at least twice a week, with Tuesdays and Thursdays being our main in-person days.

In addition to our unique culture, Hex proudly offers a competitive total rewards package, including but not limited to, market-benched salary & equity, comprehensive health benefits, and flexible paid time off.

The salary range for this role is: $214,000 - $285,000

The salary range shown may be a reflection of additional factors such as geographical location and skill ranges/levels we’re open to. Placement in the salary range will be decided upon completion of the interview process, taking into account factors like leaving room for growth, internal fairness & parity, your demonstrated skills, and the depth of your experience. Our Recruiting team will be able to provide more details during the interview process.

About the role

Hex is an AI-powered platform for modern Data Science and Data Analytics workflows. AI Research Engineers at Hex partner with product teams to build industry-leading AI experiences such as the Notebook Agent. In the process, AI Engineers will run experiments, fine-tune models, deploy AI infrastructure, and build and maintain experimentation tooling.

The backbone of all of our AI experiences is providing relevant context to the agent. As an AI Research Engineer focused on our search and context architecture, you’ll be responsible for building out key components of our agentic platform, from agentic search and discovery subagents to high-scale, permissions-aware indexing systems.

If you’re a strong builder excited to bring these capabilities to thousands of users, come do it on the best Data Science platform around, with the best user context.

We are looking for a senior engineer (from an AI Eng, SWE or MLE background) eager to rapidly expand our capabilities in several greenfield applications. As one of our team’s early hires, you will work on a variety of initiatives including:

• Experimenting with new agentic techniques for search, discovery, and context management
• Designing and implementing the architecture for our scalable search and indexing pipelines
• Working at the cutting edge of production AI applications deployed to real customers

This type of ownership will provide incredible opportunities for personal and professional growth. You will have ample technical and team leadership opportunities, depending on your interests.

We aim to deliver AI capabilities that radically accelerate and augment the data science workflow – we’ve got an exciting roadmap and are willing to share more details during the process. We’re particularly interested in candidates who bring inspired excitement about what is possible in this space.

We'd love to hear from you if you have:

• Experience building and measuring high quality search and recommendation systems
• Experience getting AI/ML capabilities into production and serving real users
• A lot of enthusiasm for applications of AI to real business problems.
• Understanding of core MLOps/SW Architecture concepts for modern ML-based applications.
• You’re comfortable working in both Python & JS/TS – it’s okay if you’re only strong in one, but openness to both is important.
• Experimentalist mindset – understanding that quick iteration, and discerning critical thinking, can yield rapid progress on new challenges.
• Interest in the data space, and a love of shipping great products and building tools that empower end users to do more.
• Experience maintaining a high quality bar for design, correctness, and testing.

Our stack

Our product is a web-based notebook and app authoring platform. Our frontend is built with Typescript and React, using a combination of Apollo GraphQL and Redux for managing application state and data. On the backend, we also use Typescript to power an Express/Apollo GraphQL server that interacts with Postgres, Redis, and Kubernetes to manage our database and Python kernels. Our backend is tightly integrated with our infrastructure and CI/CD, where we use a combination of Terraform, Helm, and AWS to deploy and maintain our stack.

In addition to our unique culture, Hex proudly offers a competitive total rewards package, including but not limited to, market-benched salary & equity, comprehensive health benefits, and flexible paid time off.

The salary range for this role is: $225,000-$285,000

The salary range shown may be a reflection of additional factors such as geographical location and skill ranges/levels we’re open to. Placement in the salary range will be decided upon completion of the interview process, taking into account factors like leaving room for growth, internal fairness & parity, your demonstrated skills, and the depth of your experience. Our Recruiting team will be able to provide more details during the interview process.

Method is a global design and engineering consultancy founded in 1999. We believe that innovation should be meaningful, beautiful and human. We craft practical, powerful digital experiences that improve lives and transform businesses. Our teams based in New York, Charlotte, Atlanta, London, Bengaluru, and remote work with a wide range of organizations in many industries, including Healthcare, Financial Services, Retail, Automotive, Aviation, and Professional Services.

Method is part of GlobalLogic, a digital product engineering company. GlobalLogic integrates experience design and complex engineering to help our clients imagine what’s possible and accelerate their transition into tomorrow’s digital businesses. GlobalLogic is a Hitachi Group Company.

We are seeking an experienced Principal Data Architect to join our Data & AI team. In this leadership role, you will help shape modern data and AI architecture strategies that enable organizations to achieve business outcomes through scalable, reliable, and secure data platforms. You will work closely with senior stakeholders, client teams, and engineering teams to define architectural direction, guide implementation approaches, and support the delivery of data-driven capabilities across analytics, AI, and operational use cases. You will exemplify Method culture and values by driving standards of excellence and mentoring and educating those who are accountable to you on projects or within the data engineering discipline. You will be reporting into the head of the Data & AI team. 

This role requires a strong blend of technical depth, architectural thinking, client engagement and team mentorship. You will be expected to navigate ambiguity, align business and technology priorities, and help teams deliver practical, high-quality solutions that support long-term value creation. In addition to shaping solution direction, you will play an important role in guiding and developing data engineering teams through coaching, knowledge sharing, and hands-on support.

Travel for team, client and sales meetings is required, typically up to 25%. This is a US-based position in the East Coast or Mid-Central time zone.

Job Responsibilities

• Lead complex data and AI platform initiatives, addressing architectural concerns while enabling delivery teams to meet defined standards, timelines, and budgets.
• Provide client-facing technical leadership on data architecture, data engineering, analytics platforms, and AI/ML infrastructure.
• Collaborate with enterprise architects and senior technology stakeholders to align on architectural decisions, trade-offs, and implementation approaches.
• Communicate architectural concepts clearly to both engineering teams and business stakeholders.
• Design architectures that bridge operational systems and analytical platforms, enabling data products, advanced analytics, and AI capabilities.
• Define and implement architectures for data governance, security, privacy, and regulatory compliance.
• Establish and integrate data quality frameworks, observability practices, and data reliability processes.
• Partner with sales and pre-sales teams to shape solution approaches, validate technical feasibility, and support proposals.
• Develop new value propositions and offerings for the data engineering at Method, create go to market strategies, test and refine the offerings during the sales pursuits.
• Grow and maintain the data engineering team. Provide career guidance and facilitate career development opportunities across Method. 
• Create an environment that brings together the best of various data disciplines with care for the uniqueness of each project. 
• Foster a culture of curiosity and learning, creating opportunities for employees to learn and grow into our future leaders.

Skills Required
Technical Skills

• 8+  years of experience in data engineering, with 3+ years in a technical leadership role. 
• Experience designing and implementing modern data architectures, including lakehouse, data mesh, and real-time data platforms, and evaluating technology trade-offs.
• Strong background in distributed systems and data-intensive applications.
• Experience building and operating large-scale data platforms, including data warehouses, streaming systems, and data lakes.
• Expertise in data modeling, data platform operations, observability, and reliability engineering for data systems.
• Proficiency with modern data and distributed technologies such as Spark, Kafka, Flink, Trino/Presto, Iceberg/Delta/Hudi, and distributed storage systems.
• Experience designing streaming and low-latency architectures and understanding their operational trade-offs.
• Experience designing and deploying AI/ML platforms, including feature stores, model training pipelines, and model serving systems.
• Familiarity with MLOps and ML productionization practices, including CI/CD, model monitoring, and governance.
• Strong experience working with cloud platforms (AWS, Azure, or GCP) and cloud-native data services.
• Experience with modern data platforms such as Snowflake, Databricks, or similar analytics environments.
• Understanding of data governance frameworks, data cataloging, lineage, and privacy controls.
• Experience implementing data-driven decision systems, advanced analytics, and machine learning solutions in enterprise environments.

Professional Skills

• Advise C-suite executives on data strategy, AI investment, and digital transformation at the highest organizational level.
• Strong experience supporting pre-sales, solution shaping, and technical discovery engagements.
• Support client partners in growing key accounts by identifying new opportunities, navigating complex situations, and educating clients on the value of our work beyond data and AI.
• Excellent stakeholder management skills and the ability to build trust with both business and technical leaders.
• Ability to cultivate strong partnerships across organizations to drive adoption of modern data and AI capabilities.
• Strong influencing skills, with the ability to galvanize, inspire, and align others around a shared direction.
• Contribute Thought Leadership through presentations, blogs, or public talks
• Strong conflict resolution and risk management skills.
• Team player with the capacity to mentor, guide, and champion engineering teams and architects.
• Commitment to technical excellence, pragmatic delivery, and continuous improvement. 

Why Method?

We look for individuals who are smart, kind and brave. Curious people with a natural ability to think on their feet, learn fast, and develop points-of-view for a constantly changing world find Method an exciting place to work. Our employees are excited to collaborate with dispersed and diverse teams that bring together the best in thinking and making. We champion the ability to listen, and believe that critique and dissonance lead to better outcomes. We believe everyone has the capacity to lead and look for proactive individuals who can take and give direction, lead by example, enjoy the making as much as they do the thinking, especially at senior and leadership levels.

We believe in work/life balance. Seriously. We offer a ton of competitive perks, including:

• Continuing education opportunities 
• Flexible PTO and work-from-home policies 
• Pension 
• Health and dental benefits
• Company lunches, company outings, along with a lot of snacks 
• Health and wellness programs 
• Other location specific perks (just ask!)

Next Steps

If Method sounds like the place for you, please submit an application. Also, let us know if you have a presence online with a portfolio, GitHub, Dribbble or other platform.

* For information on how we process your personal data please see Privacy.

* If you are a California resident, more details on how we process your personal information can be found in the CCPA Recruitment Privacy Notice (https://www.globallogic.com/privacy/ccpa-recruitment-privacy-notice/)"

Colorado Applicants (Colorado Equal Pay for Equal Work Act Disclosure):  The starting salary range for this role to be performed in Colorado is estimated to be between $160,000 to $200,000 annually.  This salary range is specific to Colorado residents only, and provided as a general estimate.  The amount offered may be higher or lower. Method  takes many factors into making an offer, including candidate qualifications, work experience, operational needs, travel and onsite requirements, internal peer equity, prevailing wage, responsibilities, and other market and business considerations. 

New York City Applicants (New York City Local Law 32;  Pay Transparency Law):  The starting salary range for this role to be performed in New York City is from $192,000 to $205,000. This salary range is specific to New York City residents only, and provided as a general, good faith estimate. The amount offered may be higher or lower. Method takes many factors into making an offer, including candidate qualifications, work experience, operational needs, travel and onsite requirements, internal peer equity, prevailing wage, responsibilities, and other market and business considerations.

We are seeking a highly skilled and experienced Technical AI Project Manager to lead and oversee the planning, execution, and successful delivery of software and AI development projects. The ideal candidate will have a strong background in managing multiple complex software development initiatives, ensuring they are completed on time, within scope, and on budget. A strong technical foundation, exceptional organizational skills, and the capacity to communicate effectively across technical and non-technical teams are essential for this position.

Department 

The Technology Division at Success Academy is the chief provider of technology infrastructure, application services, security, and support. The Division partners with teams across the organization to equip students, teachers, leaders, and staff with the innovative technology tools and know-how necessary to achieve academic and operational excellence.

The Technical AI Project Manager will be a key contributor to our mission of driving innovation across the organization. This role will be responsible for building and managing end-to-end project plans, monitoring and reporting progress, effectively managing scope and dependencies, proactively mitigating risks, and ensuring that projects are delivered within time, cost, and quality specifications. This role will be part of a highly collaborative team of professionals with a wide range of skills, including DevOps engineering, data engineering, software engineering, RPA engineering, and project management.

About the Role

• Acts as project lead, managing cross-functional teams with responsibilities for project schedule and performance.
• Ensures that business outcomes are defined, approved, and delivered. Be responsible for benefits realization and ensure that benefits are delivered in the best way possible.
• Supports strong governance, aligned with the overall governance, and ensures that timely decisions are taken.
• Communicates regularly and effectively the project roadmap and milestones with project executive stakeholders.
• Directly contributes to project definition, development, and/or enhancement efforts, including cost management/avoidance, schedule estimation and tracking, contract performance management, risk management, requirements analysis, and quality assurance activities.
• Performs analysis of business and user needs, documentation of requirements, and translation into proper requirements specifications.
• Utilizes expert communication skills needed to direct skilled technical resources and report on the technical progress, issues, and problem areas, as well as write and review technical documents.
• Answers technical questions regarding products and services and may take part in putting together proposals, configurations, and product plans.
• Prepares affected business areas for transition to a new way of working, potentially implementing new business processes.

Responsibilities

• Lead the end-to-end planning, execution, and delivery of software development and AI/ML projects.
• Collaborate with stakeholders to define project scope, goals, and deliverables that support business objectives, specifically translating business needs into technical requirements for software and AI solutions.
• Create and maintain detailed project plans, schedules, and roadmaps, including technical milestones for development, testing, and deployment phases.
• Monitor and track project progress, adjusting plans as necessary to ensure technical milestones are met and dependencies are managed effectively.
• Identify and mitigate technical risks (e.g., scalability issues, data quality, model performance, integration challenges) and resolve issues to keep projects on track.
• Facilitate communication and collaboration between cross-functional teams (software engineering, data science, AI/ML engineering, design, QA, product).
• Provide regular project status updates to stakeholders and upper management, clearly articulating technical progress and potential roadblocks.
• Manage project budgets, timelines, and resource allocations for technical teams.
• Ensure that all project documentation, including technical specifications, architecture diagrams, and model documentation, is complete, up-to-date, and accessible.
• Foster an agile development environment, leading sprint planning, daily standups, and retrospectives, with a focus on technical sprint goals and deliverables.
• Ensure that the development process aligns with best practices in software engineering, data governance, and responsible AI development.
• Drive continuous improvement of project management processes and tools, integrating best practices for technical project delivery.

Requirements

• Bachelor’s degree in Computer Science, Information Technology, Business Management, or a related field.
• 5+ years of experience in software project management, ideally in a mid-to-senior capacity, with a proven track record of delivering complex technical projects.
• Proven experience managing full software development life cycles (SDLC) from concept to deployment.
• Strong understanding of software development methodologies (Agile, Scrum, Kanban, or Waterfall).
• PMP, PRINCE2, or Scrum Master certification preferred.
• Experience working with tools such as Jira, Asana, Trello, Confluence, Monday.com, or similar project management software.
• Strong business analysis skills, with a focus on working with a diverse group of users to understand and solve problems.
• Ability to manage multiple projects simultaneously and prioritize tasks in a fast-paced, technically demanding environment.
• Strong problem-solving skills with the ability to make informed technical decisions under pressure.
• Proficiency in budgeting, forecasting, and resource management for engineering teams.
• Experience working with remote teams and managing distributed projects is a plus.
• Familiarity with software development practices and technical concepts, including cloud infrastructure, APIs, and version control (e.g., Git).

Desired Skills

• Experience in managing projects in cloud environments (AWS, Azure, GCP), including an understanding of cloud-native services for compute, storage, and data.
• Knowledge of DevOps practices and CI/CD pipelines, with an understanding of how to optimize release cycles for software and AI models.
• Familiarity with software testing and quality assurance processes, including automated testing and performance testing.
• Strong organizational skills with attention to detail, particularly in managing complex technical dependencies.
• Any candidate who has been part of the development team and has spent time as a developer is ideal for this role and is desired.

Technical Skills for Software Development

• Understanding of common programming languages such as Python, Java, JavaScript, C#, or Go.
• Familiarity with database technologies (e.g., SQL, PostgreSQL, MongoDB, Cassandra).
• Knowledge of front-end and back-end frameworks (e.g., React, Angular, Node.js, Spring Boot, Django, Flask).
• Experience with API design, development, and integration (RESTful, GraphQL).
• Understanding of microservices architecture and distributed systems.
• Familiarity with containerization technologies (Docker, Kubernetes).
• Knowledge of security best practices in software development (e.g., OWASP Top 10).

Technical Skills for AI Development

• Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
• Familiarity with AWS Bedrock and its available foundation model.
• Understanding of data science concepts, including statistical modeling, data preprocessing, feature engineering, and model evaluation metrics.
• Experience with data pipelines and ETL processes for machine learning (e.g., Apache Spark, Kafka, Talend).
• Knowledge of MLOps practices for deploying, monitoring, and managing machine learning models in production.
• Familiarity with natural language processing (NLP) or computer vision (CV) concepts if relevant to the projects.
• Understanding of responsible AI principles, including fairness, interpretability, and privacy.
• Experience with big data technologies and data warehousing solutions.

ABOUT THE ROLE 

Peloton is looking to transform our enterprise tech strategy with AI adoption. We are looking for a Staff Enterprise AI Engineer to serve as the "Founding Engineer" of our Enterprise AI Platform.This is not a traditional Data Science role. You will not spend your days tweaking hyperparameters. Instead, you will architect and build the Operating System that enables our Product, People, and Operations teams to deploy AI Agents safely and at scale. You will act as a "Player/Coach," laying the technical foundation (Infrastructure, Security, Orchestration) while guiding a team of engineers to execute the vision. You will build the "Golden Path" that helps everyone at Peloton to leverage AI securely for the competitive advantage of Peloton.

YOUR DAILY IMPACT AT PELOTON

• Architect the "Intelligence & Integration" Layers
• Design and build a scalable Agentic Orchestration Platform (using LangChain, LangGraph, or custom frameworks) that allows internal developers to spin up autonomous agents.
• Implement the "Integration Layer" ensuring all AI agents connect to internal APIs (Workday, Snowflake, SAP) via secure, standardized protocols (Model Context Protocol - MCP).
• Solve the "State Problem" for AI, architecting memory stores (Vector DBs like Pinecone/Weaviate) that persist context across user sessions.
• Enforce "Security by Design"
• Partner with Security leadership to implement Identity Propagation. Ensure agents execute tasks using the user’s specific OAuth scopes, preventing privilege escalation.
• Build "Data Clean Rooms" and PII masking pipelines to ensure sensitive member or employee data is never leaked to model providers.
• Deploy EvalOps pipelines to automatically test models for hallucination and regression before they hit production
• Define the Engineering Standards
• Define the "Guide vs. Control" standards for the organization. Create the templates and libraries that allow analysts to "Vibe Code" (low-code/assisted coding) safely within our guardrails.
• Perform rigorous code reviews for partner teams and vendors, ensuring high performance, low latency (<200ms), and cost efficiency
• Capital-Efficient Scale
• Optimization of inference costs by implementing Semantic Caching and routing logic (e.g., routing simple queries to smaller/cheaper models).
• Leverage Kubernetes (EKS) to manage ephemeral compute resources for AI workloads.
• A Systems Builder: You view AI as a distributed systems problem. You care about latency, rate limiting, and eventual consistency just as much as you care about prompt engineering.
• A Pragmatist: You don't build "Science Projects." You build tools that solve specific business frictions (e.g., automating Content PR approvals or speeding up Supply Chain queries).
• A Force Multiplier: You enjoy mentoring senior engineers and demystifying AI for non-technical stakeholders (from HR to Product).

YOU BRING TO PELOTON

• Experience: 10+ years of software engineering experience, with 3+ years specifically focused on MLOps, LLM Orchestration, or Large Scale Distributed Systems.
• The Stack: Deep fluency in Python (production grade) and Go (preferred for platform services).
• AI Engineering: Proven experience deploying RAG (Retrieval Augmented Generation) and Agentic Workflows in production. Experience with frameworks like LangChain, Semantic Kernel, or similar.
• Platform Engineering: Strong background in Kubernetes (EKS), Docker, and Infrastructure-as-Code (Terraform).
• Security: Solid understanding of OAuth 2.0 (OBO flow), RBAC, and zero-trust networking principles.
• Communication: Ability to explain complex technical trade-offs (e.g., "Latency vs. Accuracy") to executive stakeholders.

BONUS 

• Experience implementing Model Context Protocol (MCP) or similar standardized tool interfaces.
• Background in FinOps (managing GPU/Cloud spend).
• Experience navigating highly regulated environments (HIPAA, SOX, etc.).

About impact.com
impact.com is the world’s leading commerce partnership marketing platform, transforming the way businesses grow by enabling them to discover, manage, and scale partnerships across the entire customer journey. From affiliates and influencers to content publishers, brand ambassadors, and customer advocates, impact.com empowers brands to drive trusted, performance-based growth through authentic relationships. Its award-winning products - Performance (affiliate), Creator (influencer), and Advocate (customer referral) - unify every type of partner into one integrated platform. As consumers increasingly rely on recommendations from people and communities they trust, impact.com helps brands show up where it matters most. Today, over 5,000 global brands - including Walmart, Uber, Shopify, Lenovo, L’Oréal, and Fanatics - rely on impact.com to power more than 350,000 partnerships that deliver measurable business results.

Your Role at impact.com:

We're seeking a Senior Data Scientist to serve as an embedded Data Scientist within our Programmatic Experience Group. You'll own the design and deployment of machine learning models that optimize yield, pricing, and inventory allocation at scale — sitting at the intersection of data science, platform engineering, and marketplace economics.

This is a high-craft, high-ownership individual contributor role. You'll work end-to-end: architecting the data pipelines that feed your models, engineering the features that drive performance, and deploying real-time inference systems that make decisions at speed. Your work directly determines how effectively Impact's programmatic marketplace balances advertiser performance with publisher monetization — making this one of the highest-leverage technical roles in the business.

You'll collaborate closely with Product, Data Science, and Programmatic Delivery Engine Engineering, but you operate with significant autonomy. You're expected to bring both the modeling rigor of a data scientist and the production instincts of an ML engineer — and to be genuinely excited about both.

What You'll Do:

Yield Optimization & Pricing Models

• Design and deploy ML models that optimize auction pricing, bid shading, floor price setting, and yield across Impact's programmatic inventory.
• Build and iterate on real-time pricing algorithms that balance short-term revenue efficiency with long-term publisher and advertiser health.
• Develop and maintain feedback loops that allow pricing models to adapt to shifting market conditions, inventory mix, and demand patterns.
• Quantify the revenue impact of pricing model improvements; communicate tradeoffs between yield maximization, fill rate, and partner ROI to stakeholders.

Inventory Allocation & Supply Optimization

• Own ML-driven inventory allocation logic: routing, pacing, and matching supply to demand across partner segments, deal types, and campaign objectives.
• Build models that forecast inventory availability, demand curves, and clearing prices to support proactive allocation decisions.
• Identify and address inefficiencies in inventory utilization — including unsold inventory, suboptimal deal matching, and allocation imbalances across the publisher base.

Data Architecture & Feature Engineering

• Design and own the data infrastructure that feeds programmatic models: event pipelines, feature stores, training datasets, and real-time feature serving.
• Engineer high-signal features from auction logs, bid stream data, user signals, contextual attributes, and historical performance — at the scale of programmatic data volumes.
• Build robust data pipelines with production-grade standards: reliability, observability, versioning, and efficient reprocessing.

Real-Time Inference & Production ML

• Deploy models to production real-time inference environments; own latency, reliability, and throughput requirements for auction-time decision-making.
• Build monitoring systems that track model performance, data drift, and system health in production; define alerting thresholds and retraining triggers.
• Partner with MLOps and Platform Engineering to ensure scalable, low-latency serving infrastructure meets SLOs under high-volume auction traffic.
• Own the full model lifecycle: training, evaluation, deployment, A/B testing, and iteration.

Experimentation & Performance Measurement

• Design and execute rigorous A/B and holdout experiments to measure the causal impact of model changes on yield, fill rate, advertiser performance, and publisher revenue.
• Build evaluation frameworks that go beyond offline metrics — validating model behavior in live auction environments where feedback signals are delayed or noisy.
• Translate experimental results into clear business narratives; present findings and recommendations to Product and business stakeholders.

Self-Learning Systems & Feedback Loops

• Research and implement adaptive, self-learning components within the programmatic stack — including contextual bandits, reinforcement learning signals, and online learning approaches where appropriate.
• Design feedback mechanisms that close the loop between auction outcomes, model updates, and system behavior; reduce reliance on manual tuning and rule-based overrides.
• Stay current with advances in programmatic ML, auction theory, and online optimization; evaluate applicability to Impact's specific marketplace dynamics.

Cross-Functional Collaboration

• Serve as the primary ML technical partner for the Rubicon product and engineering teams; translate business requirements into modeling approaches and communicate technical tradeoffs clearly.
• Collaborate with Data Science peers on shared infrastructure, modeling standards, and cross-domain feature reuse.
• Document models, architectures, and experimental findings to a standard that enables review, replication, and knowledge transfer across teams.

What You Bring:

Required

• Experience: 5+ years in data science, ML engineering, or quantitative research, with at least 2+ years building and deploying ML models in programmatic advertising, ad tech, marketplace optimization, or a closely related domain (e.g., real-time bidding, dynamic pricing, auction systems).
• Programmatic & marketplace depth: Demonstrated understanding of programmatic auction mechanics (RTB, header bidding, floor pricing, deal types, bid shading) and how ML can be applied to optimize outcomes across the supply-demand stack.
• Production ML engineering: Proven ability to take models from prototype to production independently — including real-time inference, monitoring, retraining pipelines, and SLO ownership.
• Data architecture: Experience designing and building data pipelines, feature stores, and training infrastructure for high-volume, low-latency ML systems.
• Technical skills:
• Strong Python and SQL; proficiency with ML libraries (scikit-learn, XGBoost, LightGBM, PyTorch/TensorFlow) and large-scale data tools (Spark, Kafka, or equivalent streaming/batch frameworks).
• Experience with real-time feature serving and low-latency model deployment (REST APIs, gRPC, or streaming inference).
• Familiarity with production ML workflows: model versioning, drift monitoring, A/B testing, evaluation, and retraining.
• Experience processing and modeling at programmatic data scale: high-cardinality auction logs, bid stream data, impression and click events.
• Experimentation rigor: Strong grasp of causal inference and experiment design in online, delayed-feedback environments (auction holdouts, switchback tests, variance reduction techniques).
• Communication: Ability to explain complex modeling decisions and tradeoffs to Product and business stakeholders; comfortable presenting in cross-functional forums.
• Education: Bachelor's in a quantitative field (CS, Statistics, Math, Engineering, Economics, or similar); Master's/PhD preferred.

Preferred / Nice to Have

• Direct experience with SSP, DSP, or exchange-side yield optimization — particularly floor price optimization, bid landscape modeling, or deal matching algorithms.
• Familiarity with auction theory (first-price vs. second-price dynamics, optimal reserve pricing, revenue equivalence) and its practical implications for programmatic ML.
• Experience with contextual bandits, multi-armed bandits, or reinforcement learning applied to real-time decisioning problems.
• Knowledge of online learning and adaptive algorithms in production environments with non-stationary data distributions.
• Familiarity with privacy-preserving ML techniques relevant to programmatic (differential privacy, federated learning, cookieless attribution modeling).
• Experience with GCP tools (BigQuery, Vertex AI, Dataflow, Pub/Sub) and/or Databricks/Spark for large-scale event processing and model training.
• Exposure to supply forecasting, inventory management, or capacity planning in programmatic or marketplace contexts.
• Familiarity with Impact's affiliate and partnership ecosystem, or prior experience at the intersection of performance marketing and programmatic delivery.

What Sets You Apart

• Marketplace intuition. You understand programmatic auctions not just as an engineer but as an economist — you think about incentive structures, equilibrium dynamics, and how model decisions ripple through the supply-demand stack.
• Full-stack ML ownership. You're as comfortable designing a feature store schema as you are tuning a gradient boosting model or debugging a latency spike in production. You own the whole chain.
• Feedback loop thinking. You don't just deploy models — you design the systems that make them smarter over time. You think about how today's decisions become tomorrow's training signal.
• Rigor under real-world constraints. You know how to run clean experiments in environments where feedback is delayed, data is noisy, and business pressures create tradeoffs. You don't let imperfect conditions become an excuse for imprecise thinking.
• Pragmatic delivery. You ship. You balance the perfect with the production-ready, iterate fast, and know when an MVP outperforms a six-month research project.
• Collaborative depth. You build genuine technical trust with engineering and product partners — not just by having good ideas, but by following through, communicating clearly, and making the integration easy.

Salary Range: $165,000 - $185,000 per year, plus an additional 5% variable annual bonus contingent on Company performance and eligible to receive a Restricted Stock Unit (RSU) grant.

*This is the pay range the Company believes is equitable for this position at the time of this posting. Consistent with applicable law, compensation will be determined based on the skills, qualifications, and experience of the applicant along with the requirements of the position, and the Company reserves the right to modify this pay range at any time.

Benefits and Perks: 

At impact.com, we believe that when you’re happy and fulfilled, you do your best work. That’s why we’ve built a benefits package that supports your well-being, growth, and work-life balance.

• Medical, Dental, and Vision insurance
• Office-only catered lunch every Thursday, a healthy snack bar, and great coffee to keep you fueled 
• Flexible spending accounts and 401(k)
• Flexible Working: Our Responsible PTO policy means you can take the time off you need to rest and recharge. We're committed to a positive work-life balance and provide a flexible environment that allows you to be happy and fulfilled in both your career and your personal life.
• Health and Wellness: Your well-being is a priority. Our mental health and wellness benefit includes up to 12 fully covered therapy/coaching sessions per year, with additional dependent coverage. We also offer a monthly gym reimbursement policy to support your physical health.
• A Stake in Our Growth: We offer Restricted Stock Units (RSUs) as part of our total compensation, giving you a stake in the company's growth with a 3-year vesting schedule, pending Board approval.
• Investing in Your Growth: We’re committed to your continuous learning. Take advantage of our free Coursera subscription and our PXA courses.
• Parental Support: We offer a generous parental leave policy, 26 weeks of fully paid leave for the primary caregiver and 13 weeks fully paid leave for the secondary caregiver.
• Technology Financial Support: We provide a technology stipend to help you set up your home office and a monthly allowance to cover your internet expenses. 

impact.com is proud to be an equal-opportunity workplace. All employees and applicants for employment shall be given fair treatment and equal employment opportunity regardless of their race, ethnicity or ancestry, color or caste, religion or belief, age, sex (including gender identity, gender reassignment, sexual orientation, pregnancy/maternity), national origin, weight, neurodivergence, disability, marital and civil partnership status, caregiving status, veteran status, genetic information, political affiliation, or other prohibited non-merit factors.

#LI_NewYork_NewYork

The Impact You’ll Make

• Define and lead product security strategy across web, mobile, API, cloud, infrastructure, and container security — conducting threat modeling, risk assessments, and security reviews throughout the development lifecycle with a strong shift-left focus.
• Embed secure development practices by designing and implementing secure coding standards, encryption, and security testing methodologies in close collaboration with development and ML teams, ensuring products are secure, resilient, and trustworthy.
• Own Enterprise AI Security end-to-end — from securing LLM integrations, agentic pipelines, and ML model ingestion to defending against AI-specific threats (prompt injection, data poisoning, model extraction, RAG poisoning, ), building AI incident response playbooks, and red-teaming AI systems across Recursion's product surfaces.
• Secure the AI supply chain and MLOps infrastructure by vetting third-party foundation models, open-source weights, and AI APIs before production integration, and partnering with ML engineering to protect training pipelines, feature stores, and model serving endpoints.
• Champion compliance and AI governance by operationalizing frameworks such as OWASP LLM Top 10, MITRE ATLAS, NIST AI RMF, and EU AI Act requirements — collaborating with legal, privacy, and responsible AI teams to support audits and evolving regulatory expectations.
• Scale security as a force multiplier by evaluating and deploying security tooling, detecting policy violations, driving security outcomes, and ensuring security initiatives never become a bottleneck to business objectives.
• Elevate the security culture across the organization by serving as a subject matter expert, mentoring engineering teams, and leading incident response efforts from investigation through mitigation and prevention.
• Maintain the security foundation through thorough documentation — including security requirements, guidelines, and incident response plans — and hands-on penetration testing and code reviews to simulate and get ahead of real-world threats.

The Team You'll Join

• You will join a growing Information Security team at Recursion, focused on enabling Recursion to decode biology by providing world class technology services that are designed and fit for purpose. You'll collaborate with your teammates and across departments to agree on what the most important challenges and capabilities are, then figure out how to get us there.

The Experience You'll Need

• Education & Tenure: Bachelor's or Master's degree in Computer Science, Information Security, or a related field, with 10+ years of experience in product security or application security and a proven track record securing complex products.
• Security Fundamentals: Deep understanding of security principles, threats, and countermeasures as they relate to product design and development, with familiarity across standards and frameworks including OWASP, NIST, ISO/IEC 27001, and CVSS-based vulnerability prioritization.
• Offensive Security & Penetration Testing: Hands-on proficiency with penetration testing frameworks and tools (Metasploit, Burp Suite, Nmap, Wireshark), web application attack techniques (SQL injection, XSS, CSRF, OWASP Top Ten), and the ability to simulate real-world attacks and assess their impact.
• Secure Development & Engineering: Expertise in one or more programming languages (e.g., Python, Java, C++) with strong command of secure coding practices, encryption standards, and integrating security tooling into CI/CD and development workflows.
• Enterprise AI Security: Demonstrated experience securing AI/ML systems and LLM-powered or agentic products in production — including familiarity with AI attack surfaces (prompt injection, data poisoning, model extraction, membership inference, RAG poisoning) and hands-on red-teaming of AI pipelines and agentic workflows.
• AI Frameworks & Supply Chain Risk: Working knowledge of AI security frameworks (OWASP LLM Top 10, MITRE ATLAS, NIST AI RMF, EU AI Act) and experience vetting third-party foundation models, open-source weights, and AI APIs as part of a structured supply chain security program.
• MLOps & Model Security: Familiarity with securing ML infrastructure — including training pipelines, experiment tracking, model registries, and inference endpoints — and designing least-privilege access controls for AI agents with external system or tool access.
• Leadership & Communication: Excellent communication and influencing skills, with the ability to drive security initiatives across engineering, legal, privacy, and executive stakeholders and mentor teams on security best practices.
• Certifications (Highly Desirable): CISSP, OSCP, or GWAPT for core security credentialing, plus AI-focused certifications such as GAISC, Offensive ML (OffSec), or cloud provider AI security tracks (AWS/GCP).

Working Location & Compensation:

Making SLC your home base is ideal, however, we will consider remote work for this position. We ask that remote employees commit to regular on-site visits for routine work and departmental events.

At Recursion, we believe that every employee should be compensated fairly. Based on the skill and level of experience required for this role, the estimated current annual base range for this role is $186,900 to $220,400 (USD). You will also be eligible for an annual bonus and equity compensation, as well as a comprehensive benefits package. 

#LI-BO1

#LI-REMOTE

Note: We are currently recruiting for multiple positions, however please only apply for the role that best aligns with your skillset and career goals.

Who We're Looking For

As a Machine Learning Engineer in Delivery, you are a problem solver who stays anchored to impact. You are someone who can grasp advanced engineering concepts across multiple industries, and excel at working directly with customers (and often side-by-side with them on-site) to embed cutting-edge AI models into tools that are useful and used.

You’ve shipped ML systems end-to-end and at scale: you design, build and test reliable, scalable ML data pipelines; you know how to explore and manipulate 3D point-cloud and mesh data to enable geometry-aware modelling; you select the right libraries, frameworks and tools. Working at the intersection of data science and software engineering, you translate R&D and project outputs into reusable libraries, tooling and products.

With at least 2 years industry experience (post Masters or PhD) in a commercial, non-research environment. You're truly excited about taking ownership of complex work streams and guiding teams to success, while continuously improving the systems and solutions you work on to ensure they are practical, impactful and meet the evolving needs of our customers.

Note: Due to the nature of our aerospace and defense work, this position is open to US citizens only.

This Role 

As a Machine Learning Engineer, you'll work closely with our Data Scientists, Simulation Engineers, and customers to understand and define the engineering and physics challenges we are solving. You will iterate with customers and use your influence to drive decisions around reliable deployment with measurable outcomes.

• Work closely with our simulation engineers, data scientists and customers to develop an understanding of the physics and engineering challenges we are solving
• Design, build and test data pipelines for machine learning that are reliable, scalable and easily deployable
• Explore and manipulate 3D point cloud & mesh data
• Own the delivery of technical workstreams
• Create analytics environments and resources in the cloud or on premise, spanning data engineering and science
• Identify the best libraries, frameworks and tools for a given task, make product design decisions to set us up for success
• Work at the intersection of data science and software engineering to translate the results of our R&D and projects into re-usable libraries, tooling and products
• Continuously apply and improve engineering best practices and standards and coach your colleagues in their adoption

• Experience applying Machine learning methods (including 3D graph/point cloud deep learning methods) to real-world engineering applications, with a focus on driving measurable impact in industry settings.
• Experience in ML/Computational statistics/Modelling use-cases in industrial settings (for example supply chain optimisation or manufacturing processes) is encouraged.
• A track record of scoping and delivering projects in a customer facing role
• 2+ years’ experience in a data-driven role, with exposure to software engineering concepts and best practices (e.g., versioning, testing, CI/CD, API design, MLOps)
• Building machine learning models and pipelines in Python, using common libraries and frameworks (e.g., TensorFlow, MLFlow)
• Distributed computing frameworks (e.g., Spark, Dask)
• Cloud platforms (e.g., AWS, Azure, GCP) and HP computing
• Containerization and orchestration (Docker, Kubernetes)   
• Strong problem-solving skills and the ability to analyse issues, identify causes, and recommend solutions quickly
• Excellent collaboration and communication skills - with teams and customers alike
• A background in Physics, Engineering, or equivalent

Our delivery teams drive innovation to turn AI models into practical solutions - read our blog to learn more about how you’ll contribute to this exciting journey!

What we offer

Build what actually matters

Help shape an AI-native engineering company at a formative stage, tackling problems that genuinely matter for industry and society. This is work with real-world impact - and something you can be proud to stand behind.

Learn alongside exceptional people

Work with a high-caliber, collaborative team of engineers, scientists, and operators who care deeply about doing great work, and about helping each other get better. We come from diverse backgrounds, but we share a commitment to operating at the highest level and addressing some of the most complex challenges out there. If you’re ambitious, thoughtful, and driven by impact, you’ll feel at home.

Influence over hierarchy

We operate with a flat structure: good ideas win - wherever they come from. Questioning assumptions and challenging the status quo isn’t just welcomed, it’s expected.

Sustainable pace, long-term ambition

Building meaningful technology is a marathon, not a sprint. We believe in balancing focused, ambitious work with a life beyond it. Our hybrid model blends time together in our New York office with work-from-home days, giving you the flexibility to work sustainably while staying connected in person.

And it doesn’t stop there …

🚀 Equity options - share meaningfully in the company you’re helping to build.

💰 5% contribution to 401(k) - build long-term security with a strong retirement plan.

🍽️ Free team lunch 1x/week - good food, great company, and space to connect.

🏥 Private health insurance – comprehensive cover for you, offering total peace of mind.

👶 Enhanced parental leave – 3 months full pay paternity and 6 months full pay maternity leave, to provide extra flexibility during the moments that matter most.

☀️ 20 days of Annual Leave (+ Public Holidays) - because taking time to rest matters.

📈 Personal development – dedicated support for learning, development, and leveling up over time.

💪 Gympass / Wellhub (subsidized) – for you and up to 3 family members, supporting both physical and mental wellbeing.

💳 Flexible Spending Account (FSA) – set aside pre-tax dollars for eligible healthcare expenses.

🔎 Watch this space, we’re continuing to build this as we grow…

Salary range:

$150,000 - $190,000 depending on experience 
Seniority will be assessed throughout our interview process 

About the Role

Schonfeld Strategic Advisors is seeking an experienced AI Data Engineer to join our Data Engineering team. In this role, you will be responsible for designing, building, and maintaining robust data pipelines that power SchonAI, our firm's internal AI platform. You will work at the intersection of data engineering and AI, ensuring that high-quality, timely, and relevant data flows seamlessly to our AI systems to support investment professionals across the firm.

Key Responsibilities

Data Pipeline Development

• Design and build scalable, reliable data pipelines to ingest, transform, and deliver structured and unstructured data to SchonAI using Prefect.
• Develop ETL/ELT processes for diverse data sources including market data, research documents, internal databases, and third-party APIs.
• Implement real-time and batch data processing workflows to meet varying latency requirements.
• Ensure data quality, consistency, and integrity across all pipelines.

AI Data Infrastructure

• Build and maintain data infrastructure optimized for AI/ML workloads, including vector databases and semantic search systems.
• Design data schemas and storage solutions that support efficient retrieval and processing for LLM applications.
• Implement data versioning, lineage tracking, and observability for AI training and inference pipelines.
• Optimize data delivery for low-latency AI interactions and high-throughput batch processing.

Integration & Collaboration

• Partner with AI engineers, software developers, and data scientists to understand data requirements.
• Integrate with existing firm systems including risk platforms, trading systems, portfolio management tools, and research databases.
• Collaborate with infrastructure teams on cloud architecture, security, and compliance requirements.
• Work closely with business stakeholders to prioritize data sources and pipeline enhancements.

Data Governance & Security

• Implement appropriate data access controls, encryption, and compliance measures.
• Ensure adherence to data governance policies and regulatory requirements.
• Monitor and maintain data pipeline performance, reliability, and cost efficiency.
• Document data flows, transformations, and dependencies.

Required Qualifications

Technical Skills

• Programming: Strong proficiency in Python; experience with SQL and at least one other language (e.g. Java, Scala, Go, Rust)
• Data Engineering: 5+ years of experience building production data pipelines using tools like Apache Airflow, Prefect, Dagster, or similar
• Big Data Technologies: Hands-on experience with distributed computing frameworks (Spark, Flink) and modern data platforms
• Cloud Platforms: Proficiency with AWS services (S3, Kubernetes) or equivalent GCP services
• Databases: Experience with both SQL (PostgreSQL, MySQL) and NoSQL databases (MongoDB, DynamoDB, Elasticsearch)
• AI/ML Data: Understanding of data requirements for ML/AI systems, including experience with vector databases (Pinecone, Weaviate, Qdrant) and embedding pipelines

Preferred Experience

• Experience building data pipelines for LLM applications or RAG (Retrieval Augmented Generation) systems
• Familiarity with financial data sources (market data, fundamental data, alternative data)
• Knowledge of data streaming technologies (Kafka, Kinesis, Pub/Sub)
• Experience of Analytics/Warehouse/OLAP DB (BigQ, SingleStore, RedShift, ClickHouse)
• Experience with containerization (Docker) and orchestration (Kubernetes)
• Understanding of MLOps practices and tools
• Experience with data quality frameworks (Great Expectations, Deequ)

 Professional Skills

• Bachelor's or Master's degree in Computer Science, Data Engineering, or related technical field
• Strong problem-solving skills and attention to detail
• Excellent communication skills with ability to translate technical concepts for non-technical stakeholders
• Experience working in fast-paced, collaborative environments
• Self-motivated with ability to manage multiple priorities

Who we are  
Schonfeld is a global multi-manager hedge fund that strives to deliver industry-leading risk-adjusted returns for our investors. We leverage both internal and external portfolio manager teams around the world, seeking to capitalize on inefficiencies and opportunities within the markets. We draw from decades of experience and a significant investment in proprietary technology, infrastructure and risk analytics to invest across four main strategies: Quant, Tactical, Fundamental Equity and Discretionary Macro & Fixed Income.

Our Culture
At Schonfeld, we’ll invest in you. Attracting and retaining top talent is at the heart of what we do, because we believe that exceptional outcomes begin with exceptional people. We foster a culture where talent is empowered to continually learn, innovate and pursue ambitious goals. We are teamwork-oriented, collaborative and encourage ideas—at all levels—to be shared. As an organization committed to investing in our people, we provide learning and educational offerings and opportunities to make an impact. We encourage community through internal networks, external partnerships and service initiatives that promote inclusion and purpose beyond the firm’s walls.

The base pay for this role is expected to be between $225k and $275k. The expected base pay range is based on information at the time this post was generated. This role may also be eligible for other forms of compensation such as a performance bonus and a competitive benefits package. Actual compensation for the successful candidate will be determined based on a variety of factors such as skills, qualifications, and experience.

#LI-PW1

Lantern is looking for a Director of AI Engineering to lead the strategy, development, and delivery of AI and machine learning capabilities across the organization. This is a senior leadership role responsible for building and scaling a high-performing AI engineering team, setting the technical vision for ML/LLM systems, and ensuring that AI initiatives are tightly aligned with Lantern’s product strategy, care workflows, and business objectives. You will report to the VP of Engineering and work closely with executive leadership to shape how AI transforms care delivery and operations at Lantern.

In this role, you will own the end-to-end AI engineering function—from research and prototyping through production deployment and continuous improvement. You will be expected to move fast, driving rapid proof-of-concept development and delivering production-grade AI solutions on aggressive timelines. You will establish the technical roadmap, define engineering standards, build team culture, and drive execution across multiple concurrent AI initiatives. Your work will directly impact how Lantern leverages intelligent systems to improve patient outcomes, operational efficiency, and scalable growth.

Location: Hybrid - at least 3 days/wk in one of our offices:  Dallas, TX / Chicago/Evanston / New York / Washington, DC

Responsibilities:

• Define and execute the AI engineering strategy and technical roadmap, ensuring alignment with Lantern’s product, clinical, and business priorities.
• Build, lead, and scale a high-performing AI engineering team, including hiring, mentoring, performance management, and creating a culture of technical excellence and innovation.
• Drive rapid AI proof-of-concept development, moving from problem identification to working prototypes on aggressive timelines, and shepherding successful POCs into production-grade systems.
• Own the end-to-end lifecycle of ML/LLM systems—from research and experimentation through production deployment, monitoring, and continuous improvement.
• Establish and maintain best-in-class MLOps practices: experiment tracking, reproducibility, CI/CD for models, automated testing, drift detection, observability, and scalable infrastructure on Azure.
• Partner with product, clinical operations, marketing, data, and engineering leadership to identify high-impact AI opportunities and translate them into well-scoped, executable initiatives.
• Lead architecture decisions for ML/LLM systems, ensuring scalability, reliability, security, and compliance with healthcare regulations.
• Establish clear KPIs and success metrics for the AI engineering function, including model performance, delivery velocity, team health, and business impact.
• Champion responsible AI practices across the organization, including fairness, transparency, explainability, and regulatory compliance in healthcare contexts.
• Represent the AI engineering function to executive leadership, providing regular updates on progress, risks, and strategic recommendations.
• Build external visibility for Lantern’s AI capabilities through thought leadership, community engagement, and talent brand building.

Experience & Qualifications:

• Bachelor’s degree in Computer Science, Engineering, or equivalent experience; Master’s preferred.
• 10+ years of experience in software engineering or machine learning, with at least 4 years in a leadership role managing ML/AI teams.
• Proven track record of building and shipping production ML/LLM systems at scale, including end-to-end ownership from research through deployment.
• Deep technical expertise in ML frameworks (PyTorch, TensorFlow, scikit-learn), LLM technologies (prompt engineering, RAG, embeddings, fine-tuning, agents), and modern MLOps practices.
• Strong experience with cloud-native ML infrastructure on Azure, including Azure ML, Databricks, and Azure DevOps pipelines.
• Demonstrated ability to rapidly prototype AI solutions and drive them to production on aggressive timelines.
• Experience with big data ecosystems (Spark, Databricks, Delta Lake) and streaming technologies.
• Excellent leadership skills: hiring and developing high-performing teams, managing cross-functional relationships, and influencing at the executive level.
• Strong strategic thinking with the ability to balance long-term vision with near-term execution and business impact.
• Experience operating in regulated environments, ideally healthcare, with an understanding of compliance, data governance, and responsible AI principles.
• Outstanding communication skills with the ability to translate complex technical concepts for non-technical stakeholders and executive audiences.

Strong Candidates will have:

• Experience applying AI/ML capabilities in healthcare, health tech, or other regulated industries.
• Background working with clinical data, claims data, or healthcare operational workflows.
• Experience building and scaling AI teams from the ground up, including defining team structure, processes, and culture.
• Track record of delivering AI-powered products that drove measurable business outcomes.
• Experience with LLM agents, agentic workflows, tool-use orchestration, or multi-step reasoning systems.

Benefits

• Medical Insurance
• Dental Insurance
• Vision Insurance
• Short & Long Term Disability
• Life Insurance
• 401k with company match
• Paid Time Off
• Paid Parental Leave

Who We Are

PathAI's mission is to improve patient outcomes with AI-powered pathology. Our platform promises substantial improvements to the accuracy of diagnosis and the efficacy of treatment of diseases like cancer, leveraging modern approaches in machine learning and artificial intelligence. We have a track record of success in deploying AI algorithms for histopathology in translational research, pathology labs and clinical trials.  Rigorous science and careful analysis is critical to the success of everything we do. Our team, composed of diverse employees with a wide range of backgrounds and experiences, is passionate about solving challenging problems and making a huge impact on patient outcomes.

Where You Fit 

As the Associate Director, MLOps Lead, you will lead the team responsible for the backbone of our AI/ML Stack: the infrastructure that bridges ML research and massive-scale production. Your primary directive is to evolve our stack to meet the next scale of needs in large scale ML training & inference workloads.  

You’re someone who enjoys designing and building for reliability, relishes collaboration and technical challenges, and takes pride in making things better – without taking yourself too seriously. Our technical space is broad: high-scale AI training & inference workloads, cloud infrastructure, Kubernetes, observability, distributed systems, and a bit of everything in between.

What You’ll Do

This role is critical for driving the scalability and efficiency of our Machine Learning Operations platform with high-impact & high growth strategic initiatives. 

• Vision and Roadmap: Develop and execute the long term vision & roadmap for MLOPs team to support ML development and deployment needs across the business units. Successfully manage the tension between short-term tactical deliveries and long-term architectural transformation for future growth. 
• Team Management: Lead and mentor a team of 6-7+ high-performing engineers. Strategically allocate resources to manage support for existing services while executing key strategic initiatives.
• Cross-Functional Collaboration: Partner with leaders across machine learning, data science, product engineering, and infrastructure to proactively identify pain points, address bottlenecks, and facilitate the deployment of new solutions.
• Foundation Model Readiness: Architect the compute and storage pipelines required for ML Engineers to manage millions of slides and complex derived artifacts without data fragmentation or synchronization latency.
• Inference Modernization: Modernize the AI Product inference stack to support 5-10x growth of AI runs across global deployments.
• System Observability: Collaborate with Site Reliability Engineering (SRE) to establish comprehensive metrics covering compute under-utilization, network bottlenecks, and granular cost and turn-around-time attribution.
• Technology Refresh: Conduct "Build vs. Buy" assessments, leading "Stack Refresh" audits to benchmark our proprietary tools against best-in-class commercial and open-source alternatives to meet our future needs.

What You Bring

To be successful in this role with us, you'll at least need:

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field (or equivalent experience).
• 2-3+ years of experience managing engineering team(s), with a focus on building production-grade frameworks for MLOps or ML Infrastructure.
• Deep technical expertise with ML workloads on kubernetes, cloud computing platforms (AWS/GCP/Azure), workflow orchestration (Airflow, Kubeflow, or proprietary equivalents) and DevOps principles and infrastructure-as-code (Helm, Terraform).
• Proven experience managing petabyte-scale datasets and high-throughput production inference pipelines.
• Strong software engineering skills in complex, multi-language systems and experience with scalable service architecture.
• Use of AI assistants (e.g. CoPilot, Cursor, Claude) across platform development lifecycle.

It Would Be Great If You Also Have

• Exposure to ML frameworks like PyTorch or Scikit-learn.
• Experience with large-scale data processing frameworks (e.g. Spark, Hive, Databricks, Amazon EMR)
• Expertise in MLOps principles, including model lifecycle management, feature stores, model monitoring, and CI/CD for ML.
• Familiarity with security and compliance best practices in ML systems.

We Want To Hear From You

At PathAI, we are looking for individuals who are team players, are willing to do the work no matter how big or small it may be, and who are passionate about everything they do. If this sounds like you, even if you may not match the job description to a tee, we encourage you to apply. You could be exactly what we're looking for. 

PathAI is an equal opportunity employer, dedicated to creating a workplace that is free of harassment and discrimination. We base our employment decisions on business needs, job requirements, and qualifications — that's all. We do not discriminate based on race, gender, religion, health, personal beliefs, age, family or parental status, or any other status. We don't tolerate any kind of discrimination or bias, and we are looking for teammates who feel the same way.

The cash compensation outlined below includes base salary or hourly wage and on-target commission for employees in eligible roles. The summary below indicates if an employee in this position is eligible for annual bonus, overtime pay and equity awards. Individual compensation packages are tailored based on skills, experience, qualifications, and other job-related factors. 

Annual Pay Range:
AD, MLOps: $181,500 - $278,300

Not Overtime Eligible

Eligible for Equity

Who We Are

PathAI's mission is to improve patient outcomes with AI-powered pathology. Our platform promises substantial improvements to the accuracy of diagnosis and the efficacy of treatment of diseases like cancer, leveraging modern approaches in machine learning and artificial intelligence. We have a track record of success in deploying AI algorithms for histopathology in translational research, pathology labs and clinical trials.  Rigorous science and careful analysis is critical to the success of everything we do. Our team, composed of diverse employees with a wide range of backgrounds and experiences, is passionate about solving challenging problems and making a huge impact on patient outcomes.

Where You Fit 

We are seeking Machine Learning Engineers (Applied Research & Model Development) to join our team and tackle unique machine learning challenges to advance medicine and improve patient care. You will work closely with teams across biomedical data science, product development, translational research, MLOps, and platform engineering to develop and deploy machine learning models for our AI products and services.

What You’ll Do

As an MLE (Applied Research & Model Development) at PathAI, your responsibilities will grow in scope as you progress through levels:

• Design, develop, and deploy machine learning models for research and product development projects.

• Collaborate cross-functionally with scientists, engineers, and product teams to translate biological and clinical requirements into scalable ML solutions.

• Contribute to experimental design and analysis, including ideation, documentation, and reporting.

• Participate in knowledge sharing and team initiatives (e.g., design reviews, journal clubs, ML best practices, governance activities).

• Improve ML pipelines and infrastructure in partnership with MLOps and platform teams.

• Publish and present scientific work, supporting abstracts, manuscripts, and conference contributions.

Level-specific expectations:

• MLE I: Contribute to projects with guidance, implement models, and learn best practices.

• MLE II: Independently deliver on projects, improve processes, and mentor junior engineers.

• MLE III: Lead initiatives end-to-end, set technical direction, and identify new opportunities with clear business and scientific impact.

You will have the opportunity to work in a company where all employees put patients first. We believe that every team member provides valuable contributions to our success, and no task is too small for anyone if it's important to our company goals.   Every PathAI employee is a contributor to our mission to pioneer better patient care by providing the best, most innovative AI tools to biotech, pathologists, clinicians and healthcare organizations. You will work alongside and with leading innovators in the field of AI and medicine and you will play a critical role in product development to impact patient outcomes.

What You Bring

We welcome applicants across all MLE levels. Minimum qualifications differ by level:

MLE II:

• Master’s degree plus 2–4 years of experience, or Ph.D. with 0–2 years of experience.

• Proven track record of developing and deploying machine learning models into production or research applications.

• Strong proficiency in Python, ML frameworks, and data pipeline development.

• Demonstrated ability to work independently on projects, contribute to experimental design, and improve ML workflows.

• Strong communication skills and ability to collaborate across scientific and engineering teams.

MLE III 

• Master’s degree plus 5+ years of experience, or Ph.D. with 3+ years of experience.

• Deep expertise in ML, computer vision, or biomedical AI, with a history of high-impact contributions (publications, open-source, or products).

• Mastery of ML frameworks, software engineering best practices, and deployment pipelines.

• Ability to lead end-to-end projects, mentor others, and set technical direction.

• Experience articulating technical improvements into business or clinical impact.

• Strong record of contributions to scientific strategy (abstracts, manuscripts, conference presentations).

We Want To Hear From You

At PathAI, we are looking for individuals who are team players, are willing to do the work no matter how big or small it may be, and who are passionate about everything they do. If this sounds like you, even if you may not match the job description to a tee, we encourage you to apply. You could be exactly what we're looking for. 

PathAI is an equal opportunity employer, dedicated to creating a workplace that is free of harassment and discrimination. We base our employment decisions on business needs, job requirements, and qualifications — that's all. We do not discriminate based on race, gender, religion, health, personal beliefs, age, family or parental status, or any other status. We don't tolerate any kind of discrimination or bias, and we are looking for teammates who feel the same way.

The cash compensation outlined below includes base salary or hourly wage and on-target commission for employees in eligible roles. The summary below indicates if an employee in this position is eligible for annual bonus, overtime pay and equity awards. Individual compensation packages are tailored based on skills, experience, qualifications, and other job-related factors. 

Annual Pay Range:
Machine Learning Engineer II: $107,250 - $164,450
Machine Learning Engineer III: $130,500 - $200,100

Not Overtime Eligible

Eligible for Equity

About The Role

We are seeking a highly skilled and driven Software Engineer with a strong background in full-stack development, particularly in backend technologies and Agentic AI, to join our AI/ML team. In this role, you’ll work at the intersection of engineering, data science, and real-world impact, partnering directly with clients and internal stakeholders to design and deploy ML-powered tools that solve meaningful problems.

This role combines hands-on model development with robust backend engineering and infrastructure work. You’ll help build scalable systems, support R&D initiatives, and ensure rapid iteration and deployment of machine learning solutions in dynamic, production-ready environments. You will work embedded with our top clients and the client teams to conduct use case discovery with senior stakeholders directly, developing solutions that address complex client needs. This will require being onsite with clients 3-4 days a week on a regular basis in New York.

What You’ll Do

• As part of the Forward Deployed Engineering team, you’ll contribute during a phase of rapid growth as we focus on scaling models and improving platform performance. You’ll help build backend systems, support client-facing deployments, and enable smoother workflows for machine learning solutions.
• Develop and Maintain AI/ML Systems: Build robust, scalable backend systems that support machine learning operations and data processing pipelines.
• Cloud Operations and Management: Oversee and optimize cloud infrastructure to ensure efficient deployment and operation of ML models.
• Problem Solving: Independently explore and address complex problem spaces to improve system capabilities and performance without extensive guidance.
• Cross-Functional Collaboration: Work closely with ML engineers and data scientists to integrate advanced ML technologies, ensuring seamless operations across various platforms.
• Client Engagement: Collaborate directly with Invisible’s  clients, working embedded with client teams to support use case discovery, product development, and AI deployment.
• Innovation and R&D: Actively participate in research and development of new tools that can enhance our AI capabilities and workflows.

What We Need

• 8+ years of software engineering experience, with a strong focus on ML engineering and deploying machine learning models in production.
• Extensive experience in full-stack development, particularly in backend environments that support AI/ML workloads.
• Prior experience working directly with clients in use case discovery, product development, and leading client engagements.
• Technical Expertise: Strong proficiency in Python, with deep expertise in LLMs, AI Agents, and ML model development.
• Experience designing and deploying scalable ML systems, such as retrieval-augmented generation (RAG) pipelines and production-grade AI applications.
• Extensive experience with cloud platforms (AWS, GCP, Azure) and operational best practices for ML workloads.
• Familiarity with Kubernetes and other container management tools.
• Ability to write well-structured, organized code and automated unit/E2E tests.
• Comfortable with polyglot persistence models (SQL vs. NoSQL).
• ML Operations: Experience with MLOps frameworks and best practices; familiarity with DevOps principles as applied to machine learning models, including model versioning, monitoring, and lifecycle management.
• Problem Solving: Ability to operate independently in unstructured environments, demonstrating a proactive and investigative approach to tackling challenges.
• Communication: Excellent communication skills, with the ability to collaborate effectively in dynamic, cross-functional teams, including data scientists, researchers, and software engineers.

What’s In It For You

Invisible is committed to fair and competitive pay, ensuring that compensation reflects both market conditions and the value each team member brings. Our salary structure accounts for regional differences in cost of living while maintaining internal equity.

For this position, the annual salary ranges by location are:

About the Opportunity

We are seeking a senior machine learning software engineer to design, build, deploy, monitor, and optimize production-ready ML services in regulated healthcare. You will work hands-on to package, test, orchestrate, deploy, and maintain ML models, improve workflows, and implement CI/CD and automated testing to ensure reliability, performance, and faster delivery of business value.

Responsibilities

• Collaborate with AI scientists to package and deploy ML models, ensuring reproducibility, versioning, and compliance.
• Build and maintain model serving infrastructure including monitoring, drift detection, automated retraining, and logging.
• Implement unit, integration, and system-level testing for ML models, covering data validation, model correctness, and deployment workflows.
• Develop and operate end-to-end ML pipelines: ingestion → preprocessing → feature engineering → evaluation → deployment → monitoring.
• Integrate CI/CD and MLOps practices for automated model builds, testing, and deployment.
• Identify and resolve workflow inefficiencies or gaps between research and production.
• Recommend and integrate frameworks, libraries, and infrastructure to improve pipeline efficiency, maintainability, and observability.
• Collaborate cross-functionally to ensure compliance with regulatory requirements (FDA/HIPAA) in production ML workflows.
  
  

Requirements

• 7+ years of experience in software engineering for ML production or ML platform delivery.
• Hands-on experience deploying ML models via APIs, batch pipelines, or streaming inference.
• Proficiency in Python (required), Java, or similar, with software engineering best practices for ML workflows.
• Experience with unit, integration, and pipeline-level testing for ML models, including data validation, correctness checks, and reproducibility.
• Familiarity with cloud platforms (AWS preferred: SageMaker, S3, EC2) and reproducible ML pipelines.
• Experience with CI/CD, Orchestration tools (Airflow, MLflow, Kubernetes, Terraform) and ML/data platforms(SageMaker, Databricks, Unity Catalog, Snowflake/Snowpark) to build scalable ML data pipelines and model workflows.
• Strong collaboration skills to work effectively with AI scientists, software engineers, and regulatory teams.

The base salary range for this role varies by location and is aligned to market benchmarks.

• Candidates located in higher-cost labor markets, including California, Washington, New York, New Jersey, Connecticut, Massachusetts, and Washington, DC represent the middle to high end of the range, while candidates located in all other U.S. locations represent the low to middle end of the range.
• Final compensation is determined based on location, experience, skills, and internal equity.

This role is eligible for a 15% target annual bonus, resulting in the following base salary and Total Target Compensation (TTC) ranges:

• Base Salary: $153,000 - $179,000
• TTC: $176,000 - $206,000

*Total Target Compensation (TTC): Total Cash Compensation (including base pay, variable pay, commission, bonuses, etc.) Additionally, stock options, paid benefits, and employee perks are part of your total rewards.

Who You Are

As a Senior AI Operations Engineer, you are a forward-thinking technologist who bridges the worlds of infrastructure, automation, and artificial intelligence. You thrive in dynamic, data-rich environments and bring a disciplined, service-oriented mindset to scaling AI systems and automations across Justworks. You understand how to operationalize machine learning and generative AI technologies responsibly and effectively, driving measurable business value while maintaining our commitment to reliability, security, and transparency.

You are a proactive problem-solver and trusted technical partner to Digital Security, Data and Analytics, and Engineering stakeholders. You excel at designing, implementing, and maintaining AI-driven workflows that enhance productivity, automate support processes, and integrate seamlessly into our enterprise systems. Your curiosity drives continuous learning, and your expertise enables you to mentor others in emerging AI operations practices.

Your Success Profile

What You Will Work On

• Architect, deploy, and manage AI infrastructure, workflows, and automations that power IT and internal operations.
• Develop, monitor, and optimize AI models and pipelines used for IT support automation, knowledge retrieval, and internal tooling.
• Partner with IT Systems, Data Engineering, and Security teams to ensure responsible, compliant, and ethical AI deployment.
• Integrate AI and automation tools into Justworks’ SaaS ecosystem — including Okta, Slack, Atlassian, and other enterprise platforms.
• Define and maintain standard operating procedures for AI workflows, including monitoring, model retraining, and incident response.
• Collaborate with cross-functional teams to assess opportunities for automation using LLMs, RPA, and AI service APIs.
• Serve as a subject matter expert and mentor on AI operations practices, guiding the IT team through adoption and experimentation.
• Contribute to internal documentation, training, and governance frameworks for AI systems across the organization.
• Stay current with emerging AI operations technologies, frameworks, and best practices — sharing insights to strengthen our IT capabilities.

How You Will Do Your Work

As a Senior AI Operations Engineer, how results are achieved is as important as the results themselves. Your foundational competencies include:

Good Judgment — Applying critical thinking to evaluate the tradeoffs of automation and AI deployment; balancing innovation with risk.
Resourcefulness — Building creative, effective solutions using available tools and data; adapting quickly to evolving AI technologies.
Teamwork and Communication — Collaborating across IT, Security, and Data teams with clarity and empathy; documenting rigorously.
Influence and Leadership — Mentoring peers, promoting responsible AI adoption, and fostering a culture of knowledge sharing.
Skills and Knowledge — Combining deep technical expertise with continuous learning in AI systems engineering, MLOps, and automation frameworks.

In addition, all Justworkers focus on aligning their behaviors to our core values known as COGIS. It stands for:

• Camaraderie - Day to day you can be seen working together toward a higher purpose. You like to have fun. You’re an active listener, treat people respectfully, and have a strong desire to know and help others.
• Openness - Your default is to be open. You're willing to share information, understand other perspectives, and consider new possibilities. You’re curious, ask open questions, and are receptive to thoughts and feedback from others.
• Grit - You demonstrate grit by having the courage to commit and persevere. You’re committed, earnest, and dive in to get the job done well with a positive attitude.
• Integrity - Simply put, do what you say and say what you'll do. You’re honest and forthright, have a strong moral compass, and strive to match your words with your actions while leading by example. 
• Simplicity - Be like Einstein: “Everything should be made as simple as possible, but no simpler.”

Qualifications

• 5+ years of experience in IT Systems Engineering, DevOps, or AI/ML Operations.
• Hands-on experience with AI workflow orchestration tools (e.g., LangChain, Airflow, Prefect, or similar).
• Proficiency in Python, API integrations, and automation scripting for systems and service management.
• Experience managing LLM-based or ML-powered applications in production environments.
• Knowledge of Okta, Atlassian, and SaaS automation platforms (e.g., BetterCloud, Torq, Zapier for Enterprise, or similar).
• Familiarity with cloud environments (AWS, Azure, GCP) and CI/CD best practices.
• Experience building secure and auditable automations aligned with compliance and governance standards.
• Proven record of mentorship, technical leadership, and cross-functional collaboration.
• Passion for continuous learning in AI and automation, with a strong sense of accountability for outcomes. 
  
  The base wage range for this position based in our New York City Office is targeted at $127,000.00 to $152,500.00 per year.