Overview

Caper Carts are AI-powered, intelligent shopping carts developed by Instacart that let customers scan, weigh, and pay for items directly on the cart—eliminating checkout lines. Equipped with cameras and sensors, these carts automatically recognize items, offer personalized promotions, and feature a touchscreen for real-time, interactive shopping. This machine learning team builds the brain behind the cart.

We’re hiring an Engineering Manager, Machine Learning and Computer Vision to lead a team of talented CV, ML and AI infrastructure engineers who power perception, multimodal understanding, and edge inference for Caper Carts. You will own the roadmap for how our carts see and reason about what’s in the basket, and you’ll build the platforms and models that make checkout seamless in dynamic, real-world retail environments. Your direct team will be ~10 engineers within a broader organization of ~30 spanning Android and hardware.

This is a high-impact role at the frontier of physical AI—bridging edge devices in stores with cloud-scale data and training systems. You’ll partner closely with Android, hardware, product, and operations to deliver measurable improvements in recognition accuracy, latency, and reliability. The role is remote across Canada; West Coast time zones are ideal, but we’re open to great talent anywhere in the country. Learn more about our work at Connecting stores from edge to cloud: reinventing retail with physical AI.

About the Job

• Lead and grow a team of ~10 ML, CV and AI infrastructure engineers building the perception and reasoning systems that power Caper Carts in live retail environments.
• Define the technical vision, roadmap, and success metrics for cart perception and multimodal understanding; prioritize work that drives measurable gains in item recognition accuracy, checkout speed, and system reliability.
• Architect scalable training, data, and inference platforms on GCP using Ray, Kubernetes, and modern MLOps practices to enable rapid experimentation and safe, repeatable deployments.
• Deliver production-grade CV/VLM models for multi-camera item detection, weighing, and basket reasoning; optimize on-device inference for low-latency, high-availability operation at the edge.
• Build the data flywheel end-to-end—instrumentation, labeling, evaluation, offline/online testing, and monitoring—to continuously improve performance across diverse store conditions.
• Collaborate cross-functionally with Android, hardware, product, design, operations, and retailer partners; communicate risks, tradeoffs, and timelines clearly in a fast-paced, ever-evolving environment.

About You

Minimum Qualifications

• 8+ years of experience building and deploying machine learning systems, with a strong focus on computer vision in production environments.
• 2+ years of experience managing teams of 6+ ML/CV/AI engineers, including hiring, performance management, and career development.
• Hands-on expertise with computer vision, deep learning (e.g., PyTorch), model training/evaluation, and MLOps practices for reliable CI/CD of ML services.
• Proven experience architecting and operating ML infrastructure on GCP (e.g., GKE, Vertex AI, BigQuery) and distributed training/inference with Ray; containerization with Docker and orchestration with Kubernetes.
• Experience delivering real-time edge inference, including model optimization (e.g., TensorRT, ONNX, quantization) and monitoring for latency, throughput, and accuracy.
• Proficiency in Python and SQL, with a track record of shipping end-to-end CV systems including data pipelines, experimentation, deployment, and post-launch iteration.
• Bachelor’s degree in Computer Science, Electrical/Computer Engineering, or a related technical field, or equivalent practical experience.

Preferred Qualifications

• Experience integrating on-device ML with Android applications and collaborating closely with Android teams on SDKs and APIs.
• Background with multimodal vision-language models (VLMs) and large language models (LLMs) for perception, retrieval, or instruction-based reasoning.
• Experience with sensors and hardware integration (e.g., multi-camera setups, weight sensors), calibration, and dataset generation for robotics or retail environments.
• Demonstrated success leading cross-functional programs across 3+ partner teams and delivering multi-quarter roadmaps.
• Graduate degree (MS/PhD) in a relevant field with research or applied focus in computer vision, machine learning, or robotics.

#LI-Remote

Who we are

The real world is the next frontier, and at Metropolis, we are creating the artificial intelligence to make it responsive. We are pioneering the Recognition Economy — a future where mundane repetition disappears and being known unlocks access, comfort and belonging everywhere you go. From transforming parking into a seamless drive-in, drive-out experience for millions of Members to expanding our intelligence layer across retail and hospitality, we are building a world that feels instinctive and magical. The future isn’t coming; it’s here, and we need builders, innovators and problem solvers to help us create it.

Who you are

Metropolis is seeking a Senior Manager of Machine Learning Engineering within the Advanced Technologies Group to lead the technical vision and execution of our foundational systems that power our next generation of AI. You will oversee 4 critical pillars within the Machine Learning org:data engineering, annotation pipelines, ML Infrastructure and Deployment of Agentic AI solutions. You are a hands-on, senior technical leader with a broad dynamic range, capable of providing high-level strategic direction while remaining technically proficient enough to dive into the weeds with your team. Your mission is to transition state-of-art models into robust, autonomous production systems that automate complex enterprise workflows.You will partner closely with internal engineering teams and external vendors to build the scalable tools and data pipelines that define the future of recognition economy.

What you'll do

• Build and maintain scalable, compliant and auditable data infrastructure to serve computer vision and AI pricing use cases 
• Build scalable data engineering pipelines and automated annotation workflows (LLM-in-the-loop) to reduce reliance on manual labeling and accelerate model iteration
• Own the MLOps lifecycle, including distributed training infrastructure, model registries, and low-latency inference services. Ensure high availability and observability for all deployed models
• Define technical direction, lead and grow a high-performance team of data and ML infrastructure engineers to influence impactful business outcomes
• Develop foundational systems to productionize agentic AI, Large Language Models (LLMs) and Vision Language Models (VLMs) solutions for workflow automation to enhance our products
• Enable Metropolis’s move into personalization and targeted advertisement through innovative ML data pipelines and feature stores
• Collaborate with external vendors and annotation platform providers to ensure high-quality data for production models
• Partner with other ML leaders (Growth , Edge deployment) and cross-functional leaders in Hardware, Platform, and Product engineering to align development roadmaps

What we're looking for

• 10+ years of professional experience in data and machine learning engineering with proven expertise in building enterprise-scale, auditable ETL pipelines and data governance mechanisms
• 5+ years of experience in leadership and management, ideally having managed other managers
• MS or PhD in computer science and/or a quantitative discipline
• Strong experience in distributed data processing like Apache Spark, Kafka, Cloud native data storage and processing services
• 1+ years experience building data /eval pipelines and deploying agentic AI solutions (LLMs and/or VLMs)
• Experience managing technical programs, defining milestones, and communicating progress to diverse audiences
• Familiarity with deep learning frameworks such as TensorFlow or PyTorch
• Strong proficiency with SQL and Python
• Engage effectively with external data providers and vendors
• Familiarity with computer vision systems and models (e.g. object detection, tracking, segmentation)

While not required, these are a plus:

• Manage large scale datasets and database tools for data processing
• Deploy ML services to the cloud with a focus on scalability and reliability
• Operate in innovative, high-growth environments

4 Days in Office: Metropolis values in-person collaboration to drive innovation, strengthen culture, and enhance the Member experience. Our corporate team members hold to our office-first model, which requires employees to be on-site at least four days a week, fostering organic interactions that spark creativity and connection

When you join Metropolis, you'll join a team of world-class product leaders and engineers, building an ecosystem of technologies at the intersection of parking, mobility, and real estate. Our goal is to build an inclusive culture where everyone has a voice and the best idea wins. You will play a key role in building and maintaining this culture as our organization grows. The anticipated base salary for this position is $200,000.00 USD to $250,000.00 USD annually. The actual base salary offered is determined by a number of variables, including, as appropriate, the applicant's qualifications for the position, years of relevant experience, distinctive skills, level of education attained, certifications or other professional licenses held, and the location of residence and/or place of employment. Base salary is one component of Metropolis’s total compensation package, which may also include access to or eligibility for healthcare benefits, a 401(k) plan, short-term and long-term disability coverage, basic life insurance, a lucrative stock option plan, bonus plans and more. #LI-AR1 #LI-Onsite

Location: Cincinnati

Travel Required: 2-3 times yearly 

Job Summary 

The Data Science team at PatientPoint works on business problems across the enterprise, such as provider growth, pricing, operations, marketing, campaign measurement, and customer attrition. As a Data Scientist, you’ll impact the organization by creating data products that synthesize available data into useful predictions, enable manual processes to be automated, and provide digestible information for human-in-the-loop decisions. 

What You’ll Do  

• In this role, you’ll work with a cross-functional team to enhance PatientPoint’s durable provider growth strategy.  
• You’ll be a core member of the team responsible for modeling the value of providers in the market and creating data pipelines that prioritize recruitment.  
• To be successful, you’ll develop a deep understanding of the goals and constraints of the provider growth team and leverage machine learning, analysis, and data storytelling techniques to move the business towards its goals.  
• To stay organized, you’ll work in Git, participate in code reviews, and keep your work documented (Jira, Confluence, READMEs). 
• To maximize impact, you’ll proactively share insights with your cross-functional collaborators and data science team.   

What We Need  

• Bachelors degree in Applied Statistics, Computer Science, Operations Research, Business Analytics, Information Systems or a related field.
• 2+ years of related data science experience developing data products, deploying models to production, and delivering analyses to internal and external stakeholders. 
  • Candidates will be asked to provide examples from their previous experience and/or complete a project using analytics to create actionable insights.  
• Fluency in Python and SQL, with end-to-end data stack experience including manipulation, analysis, visualization, model deployment, and pipeline orchestration. 
• Strong working knowledge of machine learning and data science methods including model training, model validation and selection, backtesting, and statistical testing. 
• Experience taking models from development to production, monitoring performance, and iteratively improving over time. 
• Strong communication skills and a collaborative attitude for working with stakeholders. 

Desired Qualifications  

• Masters or doctoral degree in Applied Statistics, Computer Science, Operations Research, Business Analytics, Information Systems or a related field . 
• Professional experience in healthcare, pharmaceutical, and digital advertising industries. 
• Strong grasp of MLOps practices and software engineering principles. 
• Experience with Snowflake, Airflow, HEX, Cursor, Claude Code. 

What You'll Need to Succeed  

• Curious self-learner that enjoys staying current on emerging methods and trends and sharing with the team and others around them. 
• Highly self-motivated and self-directed. 
• Critical thinking with the ability to identify and scope and solve ambiguous problems in a fast-paced environment. 
• The ability to empower others through data storytelling - translating complex data findings into compelling narratives. Comfortable not only presenting the data but also explaining the insights in a context that resonates with the audience.  
• Strong interpersonal, communication, and consulting skills. Ability to establish and maintain successful cross functional relationships and collaborate closely with multiple teams including engineering, product management, and business stakeholders.  
• Enthusiasm for responding to a dynamic range of questions and analytical challenges.   

#LI-ED1 #LI-Hybrid

At Ensono, our Purpose is to be a relentless ally, disrupting the status quo and unleashing our clients to Do Great Things!  We enable our clients to achieve key business outcomes that reshape how our world runs. As an expert technology adviser and managed service provider with cross-platform certifications, Ensono empowers our clients to keep up with continuous change and embrace innovation.

We can Do Great Things because we have great Associates. The Ensono Core Values unify our diverse talents and are woven into how we do business. These five traits are the key to achieving our purpose.

Honesty – Reliability – Curiosity – Collaboration - Passion

About the role and what you'll be doing: 

Ensono is building a new portfolio of Managed AI Services, and we need a Product Architect to design the technical foundation. This is a senior, hands-on role where you'll architect how our AI services are built, integrated, and delivered to enterprise clients.

You'll work at the intersection of cloud platforms, AI/ML technologies, and managed services operations—designing solutions that integrate third-party AI capabilities (Azure OpenAI, AWS Bedrock, etc.) into scalable, secure, enterprise-grade managed offerings. This isn't about building AI models from scratch; it's about architecting the infrastructure, integrations, and operational frameworks that allow Ensono to deliver AI services reliably to clients.

This is not a client-facing role, but at times you'll engage with customers to understand their needs, translate those into technical architectures, and collaborate closely with Security, the CTO office, and Operations to ensure our services are production-ready.

If you have deep experience in cloud architecture, MLOps, and the managed services business model—and you want to shape a new product line from the ground up—this is your role.

Technical Architecture (70%)

• Design the end-to-end technical architecture for Ensono's Managed AI Service portfolio, including infrastructure, integrations, and operational tooling
• Architect solutions that integrate third-party AI platforms (Azure OpenAI, AWS Bedrock, Google Vertex AI) into Ensono's managed services delivery model
• Define reference architectures for MLOps pipelines, including model deployment, monitoring, drift detection, and retraining orchestration
• Design AI-specific observability and monitoring frameworks that enable proactive managed services delivery
• Architect AI FinOps dashboards and cost governance tooling to provide clients visibility into AI spend (tokens, GPU, APIs)
• Work with security teams to define security and governance architectures for AI workloads, including prompt security, data loss prevention, and access control
• Evaluate and recommend build vs. buy decisions for platform components

Collaboration & Stakeholder Engagement (30%)

• Partner with Security to ensure AI architectures meet enterprise security and compliance requirements
• Work with the CTO office to align AI service architecture with Ensono's broader technology strategy
• Collaborate with Operations to ensure architectures are operationally supportable at scale
• Engage with Product Management to translate market requirements into technical designs
• Contribute to service definitions, SLAs, and operational runbooks

What You'll Deliver (First 6 Months)

• AI Service Reference Architecture: A comprehensive technical architecture covering infrastructure, MLOps, governance, security, and FinOps for the Managed AI Service portfolio
• Technology Stack Recommendations: Evaluated and documented recommendations for third-party tools and platforms
• Integration Patterns: Defined patterns for integrating Azure AI, AWS AI, and other non-cloud platforms into Ensono's delivery model
• Operational Blueprints: Architecture documentation that Operations can use to build and support services
• Client-Ready Collateral: Technical architecture artifacts suitable for client presentations and proposals

We want all new Associates to succeed in their roles at Ensono. That's why we've outlined the job requirements below. To be considered for this role, it's important that you meet all Required Qualifications. If you do not meet all of the Preferred Qualifications, we still encourage you to apply. 

Required Qualifications

• 10+ years of experience in technical architecture, cloud engineering, or platform engineering roles
• Managed services background: Deep understanding of how managed services are designed, delivered, and operated—you've built services that are run by operations teams, not just deployed and handed off. Previous experience at an MSP is a plus.
• Cloud architecture expertise: Strong experience with Azure and/or AWS, including compute, networking, security, and managed AI services (Azure OpenAI, Azure ML, AWS Bedrock, SageMaker)
• MLOps and AI infrastructure: Experience architecting ML pipelines, model serving infrastructure, and AI platform components
• Observability and monitoring: Experience designing monitoring, alerting, and operational visibility solutions
• Private/hybrid cloud: Experience with on-premises, hybrid, or private cloud architectures
• Client-facing experience: Comfortable presenting to technical and executive audiences, translating complex topics into clear recommendations

Preferred Experience

• Experience in regulated industries (financial services, healthcare, government)
• Background in FinOps or cloud cost management
• Experience designing multi-tenant service architectures
• Familiarity with Responsible AI frameworks and AI governance
• Category Technologies:
  • Cloud Platforms Azure (required), AWS (required), GCP (nice to have)
  • AI/ML Platforms Azure OpenAI, Azure ML, AWS Bedrock, SageMaker, Vertex AI
  • MLOps Tools MLflow, Kubeflow, Weights & Biases, Azure ML Pipelines, SageMaker Pipelines
  • LLM Orchestration LangChain, Semantic Kernel, LlamaIndex
  • Containers/Orchestration Kubernetes (AKS/EKS), Helm, Docker
  • Infrastructure as Code Terraform, Bicep, CloudFormation
  • Observability Prometheus, Grafana, Datadog, Azure Monitor, Evidently AI, WhyLabs
  • Vector Databases Azure AI Search, Pinecone, Weaviate, pgvector
  • Security Azure AD, IAM, Key Vault, network security, Zero Trust principles
• Certifications
  •  Azure Solutions Architect Expert
  • AWS Solutions Architect Professional
  • Kubernetes (CKA/CKAD)
  • Other relevant cloud or AI certifications

Why Ensono?

Ensono is a place to make better happen – for our clients and for your career. You can do great things through innovation or collaboration, by learning or volunteering, or to promote diversity and inclusion. You can do great things for your own health or for a healthier planet. Whatever it means to you to do great things we want Ensono to be the place you can do it.

We are a client-facing business, but we do encourage clients to allow us to work remotely most of the time so if you are not required to be on a client site, you can choose to work from home or in our Ensono offices.

Some of our benefits include:

• Unlimited Paid Days Off
• Three health plan options
• 401k with company match
• Eligibility for dental, vision, short and long-term disability, life and AD&D coverage, and flexible spending accounts
• Family Forming Benefit including fertility coverage and adoption/surrogacy reimbursement
• Paid childbearing and paternal leave
• Education Reimbursement, Student Loan Assistance or 529 College Funding
• Sabbatical leave
• Wellness program
• Flexible work schedule

As of the date of this posting, a good faith estimate of the current pay scale for this role is $135,000 to $180,000 annually based on a full-time schedule. Please note that placement in the range may vary based on numerous factors including but not limited to skills, experience, internal equity, and business needs. In addition to base salary, other compensation programs, depending on eligibility, include an annual bonus plan based on company and individual performance and an equity grant under our Associate Equity Appreciation Program.

Ensono is an Equal Opportunity/Affirmative Action employer. We are committed to providing equal employment to our Associates and building a diverse and inclusive workforce. All qualified applicants will be considered without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, or other legally protected basis, in accordance with applicable law.

Pay transparency nondiscrimination statement/posting OFCCP’s pay transparency policy can be found on OFCCP’s website.

If you need accommodation at any point during the application or interview process, please let your recruiter know or email USTalentAcquisition@ensono.com.

ABOUT THE TEAM

Anduril Maritime delivers platforms, systems, and integrated effects in the maritime domain. Our autonomous vehicles (sub-surface and surface) are the cornerstone of these capabilities, and we continually strive to push the boundaries of the possible in terms of endurance, autonomy and mission capability. The Maritime team develops and maintains core products and payloads, and adapts and applies those products to serve a wide variety of defense, IC and commercial customers in US and international markets.

ABOUT THE JOB 

We are seeking a Senior Machine Learning/MLOps Engineer to join the Applied Intelligence team within Maritime Digital Production. You will lead the design, deployment, and sustainment of the AI-enabled backbone that connects data, tools, and people across our digital shipbuilding environment. This role focuses on operationalizing advanced models and architecting the industrial AI stack—selecting, integrating, and standardizing technologies that bring intelligence into production systems only where they deliver real value.

You’ll define and implement core components of the platform, including unified data and feature stores, vector databases, orchestration layers, and model-serving frameworks, while integrating off-the-shelf models for computer vision, OCR/IDP, speech, and retrieval-augmented generation. You’ll work across software, data, manufacturing, and corporate technology teams to translate real factory scenarios—inspection, root-cause analysis, receiving workflows, scheduling—into applied AI capabilities with clear human-in-the-loop controls, auditability, safety gates, and integrations with PLM, MES, CMMS, ERP, and the unified data plane.

This role demands technical range, judgment, and an instinct for when AI is the right tool versus when a simpler logic or workflow solution wins. You’ll collaborate across Anduril in a high-tech, fast-paced culture of innovation focused on delivering systems that work in the real world. If you want to shape how AI becomes a practical, reliable tool for production and logistics at scale, you’ll be helping build the future of digital shipbuilding and the next generation of maritime vehicles.

WHAT YOU’LL DO 

• Architect and own the AI/ML platform stack—from data ingestion, labeling, and feature engineering to model training, deployment, monitoring, and lifecycle management.
• Select, prioritize, and standardize industrial AI components including feature stores, vector databases + RAG, OCR/IDP and CV/STT providers, orchestration layers, and observability systems.
• Build model-serving and inference frameworks optimized for production environments, supporting real-time and batch execution across cloud, edge, and shop-floor systems.
• Translate factory scenarios (receiving, inspection, RCCA, scheduling) into applied AI workflows with defined human-in-the-loop gates, audit trails, and integration contracts with PLM, MES, CMMS, ERP, and the unified data plane.
• Implement event-driven data pipelines and telemetry systems that feed models with contextualized, real-time signals from production and logistics systems.
• Drive make/buy strategy by researching internal and vendor AI capabilities and recommending investments aligned to enterprise roadmaps, Anduril IP principles, and production constraints.
• Define and maintain model governance processes for validation, safety reviews, traceability, and rollback.
• Partner with Maritime platform teams and CorpTech to align architectures with enterprise data standards, ontologies, and compliance policies.
• Lead reliability engineering for deployed models—managing drift detection, retraining triggers, alerting, and operational SLOs.
• Mentor junior engineers and data scientists; establish best practices for MLOps, observability, data management, and secure handling of sensitive production data.

REQUIRED QUALIFICATIONS 

• Strong stakeholder management skills with proven experience aligning engineering, data, and manufacturing teams.
• 8+ years of experience in software or ML engineering with end-to-end delivery of production-grade AI/ML systems.
• Deep experience with MLOps: data acquisition, labeling, curation, pipeline management, model versioning, continuous integration, and model monitoring.
• Strong proficiency in Python and experience with deep learning frameworks (PyTorch, TensorFlow).
• Experience building and deploying containerized ML services using Docker and Kubernetes.
• Proficiency in data engineering, time-series data modeling, and working with semantic/ontology-driven data systems.
• Experience implementing observability for model performance, inference accuracy, and data drift.
• Familiarity with event-driven architectures, IoT/UNS patterns, and real-time systems integration.
• Excellent communication and documentation abilities; able to bridge research, platform, and production domains.
• Eligible to obtain and maintain an active U.S. Secret security clearance.

PREFERRED QUALIFICATIONS 

• Experience applying AI/ML within manufacturing, logistics, industrial control, or production environments.
• Background with digital twins, predictive maintenance, OCR/IDP, CV, or STT model integrations.
• Experience with workflow/orchestration tools such as Flyte, Airflow, Kubeflow, or Temporal.
• Familiarity with GPU acceleration (CUDA) and inference optimization (TensorRT, Triton).
• Experience in regulated environments (NNPI/ITAR) and secure model/data governance.
• Demonstrated ability to mentor engineers and set technical direction for AI/ML infrastructure at scale.

Ads Content Understanding (ACU) is Reddit’s core commercial content understanding team for Ads. The team owns and produces signals that describe what Reddit content is about, how brand safe and suitable it is, and what users are trying to accomplish in commercial conversations. ACU is responsible for:

• The Knowledge Graph (entities, brands, products, and relationships across Reddit and external sources).
• Content taxonomies such as IAB, Shopify Standard Product Taxonomy, IAS, and other commercial taxonomies used for targeting, safety, and marketplace dynamics.
• Opinion mining for ads use cases: sentiment, stance, commercial intent, and other qualitative attributes of conversations.
• Shopping / product understanding: detecting product entities, product categories, and product attributes in organic conversations and aligning them with shopping catalogs.
• Signals and tags registry: a unified, governed catalog of ACU signals that powers retrieval, ranking, safety, and insights across Ads Foundations and partner teams. 

We are looking for a Staff Machine Learning Engineer who will lead the Commercial Content Understanding roadmap for the Monetization org and act as the technical owner for ACU’s signals and ML systems. Roughly 50% of their time should be spent in technical leadership and mentorship (driving designs, standards, cross-team alignment), and 50% in direct hands-on work (modeling, pipelines, and debugging complex production systems). 

Responsibilities:

• Provide technical leadership and mentorship to MLEs and SWEs doing ML work in ACU, acting as de facto tech lead for content understanding and signals: driving design reviews, setting technical standards, and uplifting the team’s modeling and systems craft.
• Develop evaluation systems and quality monitoring systems for content understanding signals, using SOTA LM-judge practices. 
• Drive operational excellence for ACU’s ML systems by defining SLOs, alerting, and dashboards for key signals (coverage, latency, precision/recall, cost)
• Build and evolve content understanding capabilities for commercial conversations (e.g., reviews vs. recommendations vs. comparisons vs. Q&A; sentiment and stance; product entities and categories) and operationalize them as robust signals that power contextual and shopping ads, auto-targeting, new formats, and insights products.
• Lead design and implementation of signals pipelines and produce an ACU signals registry. Partner with platform teams and other content understanding teams to ensure efficient, reliable serving at Reddit scale.
• Drive LLM and modern ML best practices within ACU: define when to prompt, finetune, or distill; design evaluation and safety harnesses; and lead at least one major distillation effort to replace external APIs with in-house models.
• Operate across the full ML lifecycle (problem framing, data, modeling, evaluation, deployment, monitoring, and oncall), designing scalable, resilient MLOps pipelines and championing responsible AI (bias, safety, explainability) for ACU’s models and signals in production.

Required Qualifications:

• 7+ years of relevant MLE experience delivering production ML systems (models + pipelines + serving) at scale, ideally in large-scale content understanding domains, or Ads. 
• Demonstrated Staff-level technical leadership: has driven architecture decisions, standards, and design reviews across multiple teams, and has aligned PMs, DSs, and engineers on shared ML systems or platforms without direct people-management authority.
• Excellent communication skills, with the ability to explain complex technical trade-offs to PMs, DSs, and other engineering teams, especially in ambiguous, cross-team problem spaces like Seekers/Searchers monetization. 
• Strong track record building and shipping NLP / Language models / content understanding models to production (e.g., classifiers, encoders, sequence or session models), with clear business outcomes (e.g., CTR/ROAS uplift, safety improvements). Experience with commercial or intent modeling is a strong plus.
• Practical experience using LLMs in production for labeling, evaluation, or distillation (e.g., LM-as-judge, prompt-based classifiers, LLM-generated labels distilled into smaller models), including managing quality, cost, and latency trade-offs.
• Deep experience with PyTorch, TensorFlow, or similar, and production-quality code in Python (and ideally one statically typed language like Go/Java/C++). Comfortable owning training, evaluation, and deployment code end-to-end.
• Experience designing ML systems and pipelines: offline training, feature pipelines (batch/streaming), online serving, monitoring, and experimentation for high-traffic surfaces.

Benefits:

• Comprehensive Healthcare Benefits and Income Replacement Programs
• 401k with Employer Match
• Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Flexible Vacation & Paid Volunteer Time Off
• Generous Paid Parental Leave 

#LI-Remote

What you’ll do

Become part of an iconic brand that is set to revolutionize the electric pick-up truck & rugged SUV marketplace by achieving the following:

• Install, configure, and maintain AI platform and tools.
• Design, deploy, and operate infrastructure supporting ML and LLM workloads.
• Build, containerize, and deploy AI services using Docker.
• Build and maintain Machine Learning CI/CD pipelines for training, testing, and deployment.
• Deploy and manage AI services across cloud and on-prem environments.
• Automate infrastructure provisioning using Terraform and Infrastructure-as-Code best practices.
• Monitor system performance, reliability, and scalability of AI platforms.
• Implement observability for agentic AI systems to ensure reliability, transparency, and debuggability.
• Perform regular security updates, patching, access control, and vulnerability remediation.
• Integrate AI tools with APIs, data platforms, and internal systems.
• Research, evaluate, and prototype new AI, LLM, and MLOps tools.
• Troubleshoot infrastructure, data, and deployment issues.
• Research, test, and prototype emerging AI technologies.
• Document system architecture, deployment processes, and operational runbooks.

Location & Travel Expectations:

• This role is based out of the Scout Motors corporate headquarters in Charlotte, NC.
• This role requires 4-5 days per week in the office, with regular in-person meetings and events. 
• Applicants should expect that the role will require the ability to convene with Scout colleagues in person and travel to participate in events on behalf of the company from time to time.

What you’ll bring 

We expect all Scout employees to have integrity, curiosity, resourcefulness, and strive to exhibit a positive attitude, as well as a growth mindset. You’ll be comfortable with change and flexible in a fast-paced, high-growth environment. You’ll take a collaborative approach to achieve ambitious goals. Here's what else you'll bring: 

• Bachelor's degree in computer science, information technology, or related field or equivalent work experience.
• 7+ years Practical experience with LLM platforms, LLMOps, and MLOps.
• Experience deploying and maintaining AI or data platforms in production.
• Hands-on experience with AI/automation tools.
• Experience designing and orchestrating agentic automation workflows.
• Strong experience with Terraform and Infrastructure-as-Code.
• Proven experience deploying applications using Docker (Docker files, image optimization, container lifecycle).
• Expertise with cloud platforms such as AWS.
• Expertise with CI/CD tooling (GitHub Actions, GitLab CI).
• Strong Knowledge of Kubernetes and orchestration.
• Solid understanding of networking.
• Experience supporting high-availability, production AI systems.
• Scripting skills (Python, PySpark, Bash).
• Knowledge of security best practices, patch management, and access controls.
• Collaborate with engineering, data, and product teams to support AI initiatives.
• Excellent problem-solving and troubleshooting skills. When a problem occurs, you run towards it not away.
• Effective communication and collaboration skills. You treat colleagues with respect. You have a desire for clean implementations but are also humble in discussing alternative solutions and options.
• A teaching and coaching approach to guiding engineers and teams in approaches.
• Minimum of High School Diploma, GED or equivalent required for all roles at Scout Motors, Inc.

What you'll gain

The benefits of joining Scout include the chance to build products and a company from the ground up.  This is a chance to create something new and lasting – with an iconic brand at its foundation.  In addition, Scout provides competitive compensation and benefits to support your physical, mental, and financial wellbeing. Program specifics are detailed in company policies and employee benefit guides, select highlights:

• Competitive insurance including:
  • Medical, dental, vision and income protection plans
• 401(k) program with:
  • An employer match and immediate vesting
• Generous Paid Time Off including:
  • 20 days planned PTO, as accrued
  • 40 hours of unplanned PTO and 14 company or floating holidays, annually
  • Up to 16 weeks of paid parental leave for biological and adoptive parents of all genders
  • Paid leave for circumstances related to bereavement, jury duty, voting time, or military leave

Pay Transparency

This is a full-time, exempt position eligible to receive a base salary and to participate in an annual performance bonus program. Final salary offered will be determined based on factors including but not limited to the candidate's skills and experience. The annual performance bonus program is preset and not candidate dependent.

Initial base salary range = $140,000.00 - $170,000.00

Internal leveling code: IC8

The GenAI Security team within Reddit’s Security, Privacy, Assurance, and Corporate Engineering organization protects Reddit’s GenAI usage across employee tools, internal agents, and production user-facing systems. Our mission is to secure and protect Reddit’s AI traffic and GenAI adoption by default.

We are building zero-trust, defense-in-depth systems that verify identity, permissions, data access, and semantic intent across AI workflows. A core part of this work is developing practical, high-quality ML models that detect and prevent security risks such as prompt injection, jailbreak attempts, sensitive data exfiltration, unsafe model behavior, anomalous usage, and unauthorized agent actions.

We are looking for a Senior Machine Learning Engineer to lead model development for GenAI Security and help establish strong ML practices across SPACE. This role owns the full machine learning lifecycle: problem definition, data ETL, feature engineering, model training, model evaluation, deployment, experimentation, prediction, monitoring, debugging, and retraining.

What You’ll Do

• Build and improve security-focused ML models for Reddit’s GenAI traffic, including guardrail models, semantic classifiers, anomaly detection models, and other neural network based security signals.
• Own model development end to end: define the security problem, assemble and label datasets, build ETL pipelines, engineer features, train models, evaluate quality, deploy to production, monitor performance, and retrain from production feedback.
• Use modern deep learning architectures, including neural networks, transformers, sequence models, embeddings, and model distillation where they are the right practical fit.
• Design rigorous evaluation suites for adversarial examples, hard negatives, long-context inputs, structured payloads, tool calls, multi-turn workflows, and real production traffic.
• Improve model precision, recall, latency, cost, calibration, and operational reliability for high-impact production surfaces.
• Build repeatable MLOps workflows for SPACE, including training pipelines, model lineage, artifact management, holdout evaluation, dashboards, rollback paths, and retraining loops.
• Partner closely with ML Infrastructure, LLM Gateway, DevX, Ads, Answers, Safety, Privacy, Compliance, and other Security teams to bring security models into real production workflows.
• Work pragmatically with Reddit’s evolving ML platform, using existing infrastructure where possible and building focused tooling when needed to keep model iteration moving.
• Translate security goals into measurable model outcomes and help partners understand tradeoffs between risk reduction, latency, false positives, and product impact.
• Provide technical direction to other engineers and serve as a go-to ML expert for GenAI Security and broader SPACE model needs.

Who You Might Be

• 5+ years of experience building, training, evaluating, and deploying production ML or deep learning models.
• Hands-on experience with modern ML frameworks such as PyTorch, TensorFlow, or similar.
• Strong practical understanding of the full ML lifecycle: problem definition, data ETL, feature engineering, training, evaluation, deployment, monitoring, debugging, and retraining.
• Experience building data pipelines and working with large-scale datasets.
• Experience designing rigorous model evaluations, including precision/recall/F1, false positive analysis, threshold tuning, calibration, holdout sets, regression tests, and production-quality validation.
• Experience shipping production-quality software, preferably in Python and/or Go.
• Strong communication skills and ability to explain model behavior, risk tradeoffs, and technical decisions to cross-functional partners.
• BS degree in Computer Science, Machine Learning, a related technical field, or equivalent practical experience.
• Experience in the following areas is a plus:
  • Applying ML to security, privacy, trust and safety, abuse prevention, adversarial ML, or GenAI security problems.
  • Training or fine-tuning neural text models for complex inputs such as long-context prompts, structured payloads, code-like content, multi-turn interactions, or tool calls.
  • Production MLOps or model serving systems such as Airflow, Ray, MLflow, Triton, ONNX, Kubernetes, or similar.
  • Improving model quality through labeling strategy, hard-negative mining, synthetic data generation, distillation, or active learning.

Benefits:

• Comprehensive Healthcare Benefits and Income Replacement Programs
• 401k with Employer Match
• Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Flexible Vacation & Paid Volunteer Time Off
• Generous Paid Parental Leave  

#LI-remote

Who We Are: 
The Machine Learning Platform team at Reddit is a high-impact team that owns the infrastructure that powers recommendations, content discovery, user and content quantification, while directly impacting other teams such as Growth, Ads, Feeds, and Core Machine Learning teams.

What You’ll Do:
As a Senior Software Engineer, you will lead the development of a large-scale GenAI Platform at Reddit.

• Contribute to the design, implementation, and maintenance of the LLM Gateway, focusing on features like unified API endpoints for internal/externally hosted LLM, rate/token limit management, and intelligent failover mechanisms to boost uptime and reliability.
• Designed and developed ML and Generative AI systems in cloud-based production environments at scale.
• Build and manage enterprise-grade RAG applications using embeddings, vector search, and retrieval pipelines.
• Implement and operationalize agentic AI workflows with tool use using frameworks such as LangChain and LangGraph.
• Drive adoption of MLOps / LLMOps practices, including CI/CD automation, versioning, testing, and lifecycle management.
• Establish best practices for observability, monitoring, evaluation, and governance of GenAI pipelines in production.
• Strong ownership mindset and platform thinking.
• Ability to lead AI platform delivery from concept to production.

Who You Might Be:

• 5+ years of experience in ML Engineering, AI Platform Engineering, or Cloud AI Deployment roles.
• Have experience operating orchestration systems such as Kubernetes at scale
• Deep experience with cloud-based technologies for supporting an ML platform, including tools like AWS, Google Cloud Storage, infrastructure-as-code (Terraform), and more
• Proficiency with the common programming languages and frameworks of ML, such as Go, Python, etc.
• Excellent communication skills with the ability to articulate technical AI concepts to non-technical stakeholders
• Strong focus on scalability, reliability, performance, and ease of use. You are an undying advocate for platform users and have a deep intuition for the genAI product development lifecycle.
• Strong knowledge of model serving, inference pipelines, monitoring, and observability for AI systems is a plus
• Strong proficiency in Python and experience with modern AI/ML frameworks (e.g. LangChain, Vertex AI Agent Builder, TensorFlow, PyTorch is a plus

Benefits:

• Comprehensive Healthcare Benefits and Income Replacement Programs
• 401k with Employer Match
• Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Flexible Vacation & Paid Volunteer Time Off
• Generous Paid Parental Leave 

Who We Are:
The Machine Learning Platform team at Reddit is a high-impact team that owns the infrastructure that powers recommendations, content discovery, user and content quantification, while directly impacting other teams such as Growth, Ads, Feeds, and Core Machine Learning teams.

What You’ll Do:
As a Staff ML Infrastructure Engineer, you will lead development of a platform for large scale ML models at Reddit.

• Design end-to-end model lifecycle patterns (MLOps) to boost velocity of development for ML engineers, including data preparation, model management, experiment tracking, and more
• Zero-to-one development and support of a graph ML codebase and platform that abstracts away common patterns and enables greater model scalability and iteration
• Collaborate with ML engineers on performance tuning, including improving model training time, efficiency, and GPU training costs in a large, distributed ML training environment
• Optimize batch data processing within a data warehouse and with tools such as Apache Beam, Apache Spark, Ray Data, and more
• Architect pipelines to build and maintain massive graph data structures on the order of billions of nodes and tens of billions of edges

Who You Might Be:

• 8+ years of experience in ML infrastructure, including model training and model deployments
• Hands-on experience with ML optimization, including memory and GPU profiling
• Deep experience with cloud-based technologies for supporting an ML platform, including tools like GCP BigQuery, Google Cloud Storage, infrastructure-as-code (Terraform), and more
• Hands-on experience administering and integrating MLOps tools for experiment tracking, model serving, and model registries (e.g. MLflow or Wandb)
• Proficiency with the common programming languages and frameworks of ML, such as Python, PyTorch, Tensorflow, etc.
• Deep experience working with distributed training frameworks, including Ray and Kubernetes
• Strong focus on scalability, reliability, performance, and ease of use. You are an undying advocate for platform users and have a deep intuition for the machine learning development lifecycle.
• Strong organizational & communication skills
• Experience working with graph databases (Neo4j, JanusGraph, TigerGraph) is a big plus
• Experience working with graph neural networks (GNNs) and associated graph ML frameworks (PyTorch Geometric, Deep Graph Library) is a big plus

Who We Are:
The Machine Learning Platform team at Reddit is a high-impact team that owns the infrastructure that powers recommendations, content discovery, user and content quantification, while directly impacting other teams such as Growth, Ads, Feeds, and Core Machine Learning teams.

What You’ll Do:
As a Senior ML Infrastructure Engineer, you will lead development of a platform for large scale ML models at Reddit.

• Design end-to-end model lifecycle patterns (MLOps) to boost velocity of development for ML engineers, including data preparation, model management, experiment tracking, and more
• Zero-to-one development and support of a graph ML codebase and platform that abstracts away common patterns and enables greater model scalability and iteration
• Collaborate with ML engineers on performance tuning, including improving model training time, efficiency, and GPU training costs in a large, distributed ML training environment
• Optimize batch data processing within a data warehouse and with tools such as Apache Beam, Apache Spark, Ray Data, and more
• Architect pipelines to build and maintain massive graph data structures on the order of billions of nodes and tens of billions of edges

Who You Might Be:

• 5+ years of experience in ML infrastructure, including model training and model deployments
• Hands-on experience with ML optimization, including memory and GPU profiling
• Deep experience with cloud-based technologies for supporting an ML platform, including tools like GCP BigQuery, Google Cloud Storage, infrastructure-as-code (Terraform), and more
• Hands-on experience administering and integrating MLOps tools for experiment tracking, model serving, and model registries (e.g. MLflow or Wandb)
• Proficiency with the common programming languages and frameworks of ML, such as Python, PyTorch, Tensorflow, etc.
• Deep experience working with distributed training frameworks, including Ray and Kubernetes
• Strong focus on scalability, reliability, performance, and ease of use. You are an undying advocate for platform users and have a deep intuition for the machine learning development lifecycle.
• Strong organizational & communication skills
• Experience working with graph databases (Neo4j, JanusGraph, TigerGraph) is a big plus
• Experience working with graph neural networks (GNNs) and associated graph ML frameworks (PyTorch Geometric, Deep Graph Library) is a big plus

Who We Are:
The Machine Learning Platform team at Reddit is a high-impact team that owns the infrastructure that powers recommendations, content discovery, user and content quantification, while directly impacting other teams such as Growth, Ads, Feeds, and Core Machine Learning teams.

What You’ll Do:
As a Senior Staff Software Engineer, you will help define and lead the vision for Reddit’s large-scale GenAI Platform, shaping the strategy, architecture, and operating model that enable teams across the company to build, deploy, and scale generative AI products with confidence.

Contribute to the design, implementation, and maintenance of the LLM Gateway, focusing on features like unified API endpoints for internal/externally hosted LLM, rate/token limit management, and intelligent failover mechanisms to boost uptime and reliability.

• Lead and execute the vision, strategy, and roadmap for Reddit’s large-scale GenAI Platform.
• Define the platform architecture and operating model that enable teams to build, deploy, and scale GenAI products reliably.
• Drive the strategy for a unified LAG Gateway supporting internally and externally hosted LLMs through consistent APIs and abstractions.
• Set the direction for core platform capabilities such as rate and token limit management, intelligent failover, and production resilience.
• Shape Reddit’s approach to an enterprise-grade RAG system
• Establish the strategic direction for agentic AI workflows and tool-use patterns across the platform.
• Own the end-to-end platform strategy from concept through production adoption and long-term evolution.
• Drive MLOps and LLMOps standards across CI/CD, testing, versioning, evaluation, and lifecycle management.
• Define best practices for observability, monitoring, governance, and operational excellence across GenAI systems.
• Partner across engineering, product, and leadership to align platform investments with company priorities and user needs.
• Champion platform thinking with a strong focus on scalability, reliability, performance, and developer experience.
• Influence technical direction across teams by turning emerging AI capabilities into a scalable platform strategy.

Who You Might Be:

• 10+ years of experience in ML Engineering, AI Platform Engineering, or Cloud AI Deployment roles.
• Have a track record of leading technical strategy and delivering AI platforms in cloud-based production environments at scale.
• Demonstrate strong execution by turning strategy into action, driving complex initiatives end to end, and consistently delivering high-quality platform outcomes.
• Bring deep experience operating Kubernetes and other orchestration systems in large-scale production environments.
• Deep experience with cloud-based technologies for supporting an ML platform, including tools like AWS, Google Cloud Storage, infrastructure-as-code (Terraform), and more
• Proficiency with the common programming languages and frameworks of ML, such as Go, Python, etc.
• Excellent communication skills with the ability to articulate technical AI concepts to non-technical stakeholders
• Strong focus on scalability, reliability, performance, and developer experience. You are an undying advocate for platform users and have a deep intuition for the genAI product development lifecycle.
• Strong knowledge of model serving, inference pipelines, monitoring, and observability for AI systems is a plus

Benefits:

• Comprehensive Healthcare Benefits and Income Replacement Programs
• 401k with Employer Match
• Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Flexible Vacation & Paid Volunteer Time Off
• Generous Paid Parental Leave 

We’re looking for a Senior Staff Machine Learning Engineer to lead Reddit’s next-generation user understanding initiative: building a unified, high-fidelity representation of each user that powers personalization across the platform.

This role requires deep expertise in mainstream ML user modeling approaches (e.g., large-scale embeddings, user interest modeling, affinities, behavioral signals) and the ability to reimagine these systems in the GenAI era—leveraging LLMs and foundation models to unlock step-change improvements in fidelity, adaptability, and expressiveness.

You will set the technical direction for this space, leading the design and implementation of Reddit’s core user representation layer—spanning embeddings, interest modeling, and key user attributes. You’ll ensure this foundation is scalable, reliable, and widely adopted across Feeds, Search, Notifications, and Ads, partnering closely with product, infrastructure, and downstream ML teams to drive measurable impact.

This is a high-impact role. The systems you build will shape how hundreds of millions of people experience Reddit every day—what they see, what they discover, and the communities they connect with. Your work will directly advance personalization and relevance at global scale, strengthening Reddit as a platform for meaningful connection and belonging.

What you'll do:

• Design User Understanding Strategy: Define a unified user understanding framework and strategy: how users are represented (embeddings, tags, attributes, LLM-based user profile), how they are computed, stored, and exposed. Provide thought leadership in user understanding and user modeling by setting a long-term technical vision and advancing the state-of-the-art in the field.
• Build Foundational User Models: Lead design and implementation of advanced user models, e.g. large-scale user representation learning (sequence-based, multi-interest, multi-task) that share representations across surfaces to improve personalization experience across key Reddit products e.g. Feeds, Notification, Search and Ads, balancing latency, cost, and performance.
• Reimagine user understanding with LLM/Gen-AI: Evolve user modeling beyond traditional representations by leveraging LLMs to build richer user understanding (e.g., dynamic user profiles, intent inference, semantic reasoning over user behavior). Explore how LLMs can augment or unify embeddings, attributes, and taxonomies to enable more adaptive, interpretable, and context-aware personalization.
• Ship Large Scale User Understanding as a System: Partner with platform teams to design and build core components for large-scale learning and serving: storage/retrieval for embeddings, feature pipelines, and APIs. Collaborate with ML/Ranking infra to ensure low-latency serving, high availability, and integration with MLOps systems.
• Drive Cross-Team Integration & Impact: Partner with Feeds, Notification, Search and Ads teams to drive experimentation and adoption of new user understanding models with product teams across Reddit, ensuring measurable end-to-end impact on key metrics.
• Set Technical Bar & Mentor: Mentor senior to staff engineers, lead design reviews, steward technical decisions across the user understanding domain, and champion and drive engineering processes and best practices

Who you might be:

• You have at least 10 years experience building and scaling production-grade ML systems, particularly in user modeling, large-scale representation learning, or recommender systems.
• You have a track record of driving ambiguous, high-impact initiatives from concept to production, shaping both technical direction and execution.
• You are product- and impact-oriented: you care deeply about how your work moves real metrics (e.g., engagement, retention, revenue), not just model quality.
• You bring strong fundamentals in mainstream user understanding ML approaches (e.g., representation learning, behavioral modeling, user clustering), and understand their trade-offs in real-world systems.
• You are excited about the GenAI shift and have experience (or strong intuition) applying LLMs or foundation models to evolve existing systems,  going beyond incremental improvements.
• You think in systems, not just models: you consider data, training, evaluation, serving, and adoption as a cohesive whole, and design with end-to-end impact in mind.
• You influence beyond your immediate team: partnering effectively with product, infra, and other ML teams, and driving alignment across multiple stakeholders.
• You raise the technical bar: mentoring senior engineers, leading design reviews, and establishing best practices for building reliable, scalable ML systems.
• You are comfortable navigating trade-offs across quality, latency, cost, and safety, especially in large-scale, user-facing systems.

Benefits:

• Comprehensive Healthcare Benefits and Income Replacement Programs
• 401k with Employer Match
• Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Flexible Vacation & Paid Volunteer Time Off
• Generous Paid Parental Leave 

#LI-remote, #LI-JS5

As Product Marketing Manager for Analytics & MLOps, you will own the go-to-market for two of Dataiku's most established product surfaces: the visual analytics and data preparation capabilities that displace the competition’s legacy tools, and the end-to-end machine learning lifecycle.

Analytics and MLOps are the proven foundation of Dataiku's enterprise platform – the capabilities that hundreds of the world's largest companies use every day to put real models and analytics into production. Your job is to sharpen positioning, drive launches, and grow share in markets where Dataiku already wins.  You'll also work with the PMM and competitive strategy teams to drive competitive battlecards, win/loss analysis, and competitive deal-desk support across Dataiku's major competitors. 

Core Responsibilities

Analytics PMM

• Position visual data prep, Flow, Stories, dashboards, and BI integrations

• Drive displacement narrative vs. legacy analytics tools

• Ship launch assets: pages, one-pagers, demos, sales talk tracks

• Maintain cadence with the Analytics product team

MLOps PMM

• Position AutoML, experiment tracking, deployment, monitoring, and CI/CD

• Build buyer narrative for DS leaders, ML platform teams, and practitioners

• Compete against full-lifecycle platforms and point solutions

• Translate productivity outcomes into proof points

Competitive Intelligence

• Own battlecard development and quarterly refreshes

• Run monthly win/loss analysis; turn insights into messaging and sales tools

Cross-functional

• Ladder Analytics & MLOps positioning to Dataiku's category story

• Regular cadence with Sales, SE, and CS for field feedback

• Collaborate on integrated launches and analyst engagements

What we're looking for

• 4–6 years B2B PMM experience in analytics, BI, or ML

• Bachelor's in marketing, business, CS, or related field

• Background with Tableau, Power BI, Looker, Alteryx, DataRobot, SageMaker, or similar a plus

• Strong storytelling, positioning, GTM strategy, sales enablement, and technical fluency in analytics/ML

#LI-LP1
 

How You’ll Make An Impact

As a Data Engineer at Boomi, you will architect the "paved road" for our AI initiatives. You will transition our "Customer Zero" prototypes from experimental code into robust, secure, and observable. You will be the bridge between cutting-edge AI research and enterprise-grade reliability, enabling the entire engineering organization to ship intelligent features faster and safer.

We are seeking a highly skilled Data Engineer to join our Agentic AI Engineering Team. This role focuses on building and operating scalable, high-quality data infrastructure that powers agentic AI systems, including LLM-based agents, multi-agent workflows, and tool-using AI systems. You will play a critical role in enabling data-driven training, evaluation, memory, observability, and continuous improvement of intelligent agents.

What You Will Do

• Architect and build scalable, secure, and observable data infrastructure to power LLM-based agents, multi-agent systems, and tool-using AI workflows.
• Design and operate robust batch and real-time data pipelines supporting embeddings, RAG systems, and agent memory frameworks.
• Develop and manage vector database solutions to enable low-latency retrieval and contextual intelligence for AI applications.
• Build data frameworks for training, evaluation, benchmarking, and continuous improvement of agentic AI systems.
• Implement strong data governance, quality controls, lineage tracking, and PII/security compliance across AI data platforms.
• Collaborate with AI/ML, platform, and DevOps teams to productionize experimental AI prototypes into enterprise-grade solutions.
• Optimize data systems for performance, scalability, reliability, and cost efficiency across cloud environments (AWS, Azure, or GCP).

The Experience You Bring

• 5+ years of experience building and operating large-scale data platforms as a Data Engineer.
• Strong programming expertise in Python and SQL for developing scalable and efficient data solutions.
• Hands-on experience designing batch and real-time data pipelines, including streaming systems like Kafka or Kinesis.
• Experience with modern data platforms and cloud environments (AWS, Azure, or GCP), including tools like Snowflake.
• Strong understanding of LLM/AI data workflows, including embeddings, RAG pipelines, evaluation datasets, and vector databases (Pinecone, Milvus ).
• Experience with DataOps/MLOps tools such as Airflow, dbt, and MLflow for orchestration and lifecycle management.
• Strong knowledge of data quality, governance, and security, including PII handling, access controls, lineage, and ensuring data reliability.

Bonus Points If You Have

• Experience supporting agentic AI systems, multi-agent architectures, or tool-using AI agents.
• Familiarity with agent memory models, conversation histories, and reasoning trace storage.
• Experience with synthetic data generation and simulation frameworks.
• Exposure to prompt engineering workflows and prompt/version management.
• Experience optimizing data systems for low-latency AI workloads.

Aren’t sure if you’re a match? We know that impostor syndrome and the confidence gap can prevent us from meeting spectacular candidates — so don’t hesitate to apply; you could be the perfect fit!

Location

Conshohocken, PA or Denver, CO (Preferred)
Or US Remote

Compensation and Benefits 

Boomi is committed to fair and equitable compensation practices. Base compensation for this position in our corporate headquarters in Conshohocken, PA ranges from $138,862 - $173,578. This position is remote-friendly and, as such, final compensation will be determined by various factors including the candidate’s knowledge, skills, experience, and geographic location. An overview of our benefits can be found here. 

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

As Director of Product Marketing, you will own the platform-level narrative that anchors the Platform for AI Success — the story that ladders every Dataiku capability to a single, differentiated category position. You will be the steward of Dataiku's platform messaging, the senior PMM voice with industry analysts, and the launch lead for the most strategically important product moments on the roadmap.

You'll partner closely with the VP of Product Marketing, the Product organization, and Sales leadership to ensure the platform narrative shows up consistently across analyst briefings, executive keynotes, customer conversations, and field enablement. You'll also lead flagship product launches that shape Dataiku's category position and require a senior PMM leader who can translate platform vision into market-ready storytelling.

This role is for a builder who can carry the torch on existing platform positioning while extending it into new markets. Dataiku is an established category leader with strong analyst recognition; your job is to sharpen and extend that position, not start from scratch.

Core Responsibilities

Platform Narrative — Own the master messaging architecture and AI Success Formula across every product, persona, and campaign. Drive differentiated positioning and maintain coherence across peer PMMs.

Flagship Launches — Lead end-to-end GTM for Dataiku's most strategic launches: positioning, messaging, sales enablement, analyst briefings, and integrated campaigns.

AI Governance — Own positioning for Dataiku's governance framework and translate evolving regulation (EU AI Act, NIST AI RMF) into sales talk tracks and customer assets.

Analyst Relations — Lead Gartner, Forrester, and IDC engagement including MQ/Wave briefings and strategic inquiries.

Executive Storytelling — Own the company narrative deck, keynote content, and competitive positioning for high-stakes customer and event conversations.

Success Profile

• 8–12 years B2B Product Marketing experience at a data, AI, ML, or enterprise software company; experience at Databricks, Snowflake, DataRobot, Alteryx, or a hyperscaler AI org is a plus

• Proven track record owning platform-level messaging for a multi-product company

• Direct experience leading flagship launches that shaped category position

• Hands-on ownership of top-tier analyst engagements (Gartner MQ, Forrester Wave)

• Deep fluency in the AI/ML stack — LLMs, agents, MLOps, governance

• Strong writer and communicator across technical and executive audiences

• Strategic operator; builder mindset; direct, low-ego collaborator

Skills & Competencies

• Deep expertise in B2B platform messaging, competitive positioning, and category creation.

• Strong fluency in the AI/ML technology stack. Comfortable discussing LLMs, agents, MLOps, governance frameworks, and the broader enterprise AI landscape with technical and executive audiences alike.

• Excellent written and verbal communication. You write narratives that move markets and brief analysts, executives, and customers with equal credibility.

• Sophisticated understanding of how to translate evolving regulation into business-relevant positioning. EU AI Act experience is a meaningful plus.

• Strategic operator who can simultaneously hold a long-arc category position and ship near-term launches against tight timelines.

• Experience contributing to or owning pricing and packaging narrative is a plus, particularly for consumption- and usage-based commercial models.

#LI-LP1
 

Guild is hiring a Senior Applied Scientist. 

The Opportunity

Join Guild at a pivotal moment as we build the AI/ML foundation that will transform how working adults navigate their education and career journeys. As a Senior Applied Scientist, you'll move beyond theoretical models to deliver production AI systems that directly improve member experiences—from augmenting our program and career data to developing agentic solutions and contributing to our Apollo platform's reinforcement learning capabilities. This is a high-impact role where your work ships to production and drives measurable outcomes for the millions of members we serve.

Here are the outcomes you will be expected to achieve:

• Deliver production AI/ML models that augment Guild's program and career data, empowering downstream product and analytics capabilities
• Launch production agentic data solutions through Databricks that improve information retrieval and synthesis while reducing operational costs
• Contribute to Guild's Apollo platform by developing AI/ML components including our reinforcement learning process and AI memory layer

Here's what you will do in this role to achieve these outcomes:

• Design, develop, and deploy production-ready AI/ML models that integrate into the product experience and drive measurable improvements in member outcomes and business performance
• Build robust evaluation frameworks including offline metrics, A/B testing, and monitoring strategies to assess model performance and business impact
• Own the end-to-end model lifecycle: deployment, monitoring, drift detection, retraining, and incident response using Guild's MLOps best practices
• Manipulate and analyze large, complex datasets using Python, SQL, Databricks, MLFlow, dbt, and Snowflake
• Partner with business stakeholders to translate strategic challenges into concrete, lasting AI/ML solutions
• Collaborate with engineering, innovation architects, and technical experts to operationalize models and stay current on emerging AI capabilities
• Influence the technical roadmap and architecture of Guild's AI platform by making principled tradeoffs across model complexity, scalability, latency, and cost
• Translate cutting-edge research (reinforcement learning, foundation models) into pragmatic, scalable solutions while exercising judgment about when ML is the right tool

You are probably a strong candidate for this role if you've accomplished:

• Shipped AI/ML models to production that drove measurable business or product outcomes (not just research or POCs)
• Built and maintained evaluation frameworks that included both offline experimentation and live A/B testing
• Developed data pipelines or ML infrastructure that scaled to support large datasets and multiple downstream consumers
• Translated ambiguous business problems into well-scoped ML solutions—and knew when not to use ML
• Worked cross-functionally with engineering, product, and business teams to operationalize technical solutions

Here are the skills/competencies you have that will be needed to achieve the expected outcomes:

• Strong proficiency in Python and SQL for data manipulation and model development
• Hands-on experience with modern ML/data platforms (Databricks, MLFlow, Snowflake, dbt or similar)
• Deep understanding of the full ML lifecycle: from experimentation through deployment, monitoring, and iteration
• Familiarity with reinforcement learning, foundation models, or agentic AI architectures
• Ability to design rigorous experiments and interpret results to inform product decisions
• Strong communication skills to partner effectively with non-technical stakeholders
• Sound judgment in balancing technical complexity with practical business constraints

We are committed to equal pay for equal work and believe in compensation transparency. All salary ranges are standardized nationwide and will not vary by region. This role offers a competitive total compensation package, including a base salary of $150,000 - $170,000 and stock options. Compensation offered will be based on a combination of factors such as experience, competencies, and internal equity.

Posting Date: May 11, 2026

*This role will stay open for a minimum of 3 days. 

About the Staff Machine Learning Engineer at Headspace:

The AI & Machine Learning group at Headspace is a dynamic and innovative group whose mission is to improve the experiences of our members and clinicians through the mindful application of AI. Our group builds conversational AI systems including Ebb, as well as search, recommendation & personalization systems that power the Headspace experience. The Staff ML Engineer for Personalization will be part of the team developing and delivering the holistic system to enable the personalization of the Headspace experience, including content recommendation, personalized nudges & notifications, and a tailored conversational AI experience with Ebb. You’ll have the opportunity to lead the vision, alignment, development, deployment, and adoption of these solutions, helping to realize Headspace’s mission to improve the health and happiness of the world.

What you will do:

• Technical Leadership: Lead the development of recommender systems for Headspace meditation & mindfulness content as well as other backend services that enable a personalized member experience including search, a user knowledge graph, and conversational AI memory. This will involve developing and deploying complex, scalable AI models and applications. Drive impactful ML technology initiatives that will shape the delivery of and access to mental healthcare. Serve as a go-to expert and mentor, exemplifying excellence in AI/ML engineering and inspiring others to pursue technical career growth.
• Shape Personalization Architecture: Contribute to the design, development, and evolution of our AI systems, taking it from high-level vision to robust implementation, enabling production-ready AI capabilities.
• Collaborative Problem-Solving: Partner with our team of software engineers,ML engineers, and MLOps engineers, and our Product & Clinical leads to build high quality features that improve members’ lives.

What you will bring:

Required Skills:

• Bachelor of Science degree or higher in Computer Science, Statistics, Mathematics or a related field OR equivalent experience
• 5+ years of ML engineering experience in an academic or professional setting, programming in Python
• 5+ years of experience with any of the following fundamental technologies: vector search, embedding models, recommender systems, supervised, unsupervised machine learning, deep learning, reinforcement learning, LLM orchestration, RAG systems.
• 3+ years of experience with modern NLP tools and machine learning libraries (scikit-learn, PyTorch, TensorFlow, spaCy)
• Experience with unit, integration, and end-to-end testing, version control
• Strong problem solving and communication skills and ability to influence across internal organizations
• Mentorship of junior engineers and contribution to DEIB initiatives

Preferred Skills:

• Master’s degree in relevant field or equivalent experience
• Professional experience with clinical and/or healthcare applications of machine learning
• Familiarity with current ML literature
• Experience with implementation of robust and highly scalable services
• Experience with AWS, including SageMaker, Lambda, S3, DynamoDB, IAM

Location: We are currently hiring this role remotely. Candidates must permanently reside in the US full-time.

For candidates with a primary residence in the greater SF area, this role will follow our hybrid model. You’ll work 3 days per week from our office, allowing for impactful in-office collaboration and connection, while enjoying the flexibility of remote work for the rest of the week. Your recruiter will share more details about our hybrid model. 

Pay & Benefits:

The anticipated new hire base salary range for this full-time position is $140,400-$200,000 + equity + benefits. 

Our salary ranges are based on the job, level, and location, and reflect the lowest to highest geographic markets where we are hiring for this role within the United States. Within this range, individual compensation is determined by a candidate’s location as well as a range of factors including but not limited to: unique relevant experience, job-related skills, and education or training. 
Your recruiter will provide more details on the specific salary range for your location during the hiring process.

At Headspace, base salary is but one component of our Total Rewards package. We’re proud of our robust package inclusive of: base salary, stock awards, comprehensive healthcare coverage, monthly wellness stipend, retirement savings match, lifetime Headspace membership, generous parental leave, and more. Additional details about our Total Rewards package will be provided during the recruitment process. 

About Headspace

Headspace exists to provide every person access to lifelong mental health support. We combine evidence-based content, clinical care, and innovative technology to help millions of members around the world get support that’s effective, personalized, and truly accessible whenever and wherever they need it.

At Headspace, our values aren’t just what we believe, they’re how we work, grow, and make an impact together. We live them daily: Make the Mission Matter, Iterate to Great, Own the Outcome, and Connect with Courage. These values shape our decisions, guide our collaborations, and define our culture. They’re our shared commitment to building a more connected, human-centered team—one that’s redefining how mental health care supports people today and for generations to come.

Why You’ll Love Working Here:

A mission that matters—with impact you can see and feel

A culture that’s collaborative, inclusive, and grounded in our values

The chance to shape what mental health care looks like next

Competitive pay and benefits that support your whole self

How we feel about Diversity, Equity, Inclusion and Belonging:

Headspace is committed to bringing together humans from different backgrounds and perspectives, providing employees with a safe and welcoming work environment free of discrimination and harassment. We strive to create a diverse & inclusive environment where everyone can thrive, feel a sense of belonging, and do impactful work together. 

As an equal opportunity employer, we prohibit any unlawful discrimination against a job applicant on the basis of their race, color, religion, gender, gender identity, gender expression, sexual orientation, national origin, family or parental status, disability*, age, veteran status, or any other status protected by the laws or regulations in the locations where we operate. We respect the laws enforced by the EEOC and are dedicated to going above and beyond in fostering diversity across our workplace. 

*Applicants with disabilities may be entitled to reasonable accommodation under the terms of the Americans with Disabilities Act and certain state or local laws. A reasonable accommodation is a change in the way things are normally done which will ensure an equal employment opportunity without imposing undue hardship on Headspace. Please inform our Talent team by filling out this form if you need any assistance completing any forms or to otherwise participate in the application or interview process.

Headspace participates in the E-Verify Program.

Privacy Statement

All member records are protected according to our Privacy Policy. Further, while employees of Headspace (formerly Ginger) cannot access Headspace products/services, they will be offered benefits according to the company's benefit plan. To ensure we are adhering to best practice and ethical guidelines in the field of mental health, we take care to avoid dual relationships. A dual relationship occurs when a mental health care provider has a second, significantly different relationship with their client in addition to the traditional client-therapist relationship—including, for example, a managerial relationship.  

As such, Headspace requests that individuals who have received coaching or clinical services at Headspace wait until their care with Headspace is complete before applying for a position. If someone with a Headspace account is hired for a position, please note their account will be deactivated and they will not be able to use Headspace services for the duration of their employment. 

Further, if Headspace cannot find a role that fails to resolve an ethical issue associated with a dual relationship, Headspace may need to take steps to ensure ethical obligations are being adhered to, including a delayed start date or a potential leave of absence. Such steps would be taken to protect both the former member, as well as any relevant individuals from their care team, from impairment, risk of exploitation, or harm.

For how how we will use the personal information you provide as part of the application process, please see: https://www.headspace.com/applicant-notice

Vast is looking for a Staff Data Architect, reporting to the Senior Director of Information Systems, to support the development of the data infrastructure and AI-driven capabilities that will be required for the design and build of artificial-gravity human-rated space stations.

This will be a full-time, exempt position located in our Long Beach location.

Responsibilities:

• Design, architect, and implement scalable enterprise data infrastructure including data lakes, warehouses, and real-time streaming platforms
• Build and maintain robust data pipelines that feed internal AI/ML tools and enable advanced analytics across all departments
• Own end-to-end data architecture from ingestion through transformation to consumption, ensuring data quality, reliability, and security
• Integrate data from complex engineering systems (PLM/Teamcenter), manufacturing systems (MES/MOM), ERP (NetSuite), IoT/sensor networks and other enterprise systems
• Create innovative data solutions that enable AI/ML capabilities for engineering analysis, manufacturing optimization, and supply chain intelligence
• Establish data governance frameworks, standards, and best practices across the organization
• Implement MLOps infrastructure to support model training, deployment, and monitoring
• Build real-time data pipelines from shop floor systems, test equipment, and operational technology (OT) environments
• Collaborate with Engineering, Manufacturing, Supply Chain, and business teams to understand data requirements and deliver BI/AI-ready datasets
• Lead and mentor a high-performing data engineering team, establishing technical standards and fostering a culture of extreme ownership
• Evaluate and implement cutting-edge data technologies including cloud-native services, vector databases, and modern data stack tools
• Optimize data pipelines for performance, cost-efficiency, and scalability
• Implement monitoring, alerting, observability, and disaster recovery capabilities for mission-critical data systems
• Provide technical leadership and strategic guidance on data architecture to executive leadership

Minimum Qualifications:

• Bachelor's degree in Computer Science, Data Engineering, Computer Engineering, or equivalent work experience
• 8+ years of hands-on experience in data engineering, data architecture, or related fields
• 3+ years of experience in a technical leadership role leading data engineering teams or initiatives
• Proven track record of building enterprise-scale data infrastructure and production data pipelines
• Expert-level proficiency in SQL and Python
• Deep hands-on experience with cloud data platforms (AWS, Azure, or GCP) and associated data services
• Strong experience with modern data processing frameworks (Apache Spark, Kafka, Airflow, or equivalent)
• Demonstrated experience with data warehousing technologies (Snowflake, Databricks, BigQuery, Redshift, or similar)
• Experience supporting AI/ML initiatives with production-grade data pipelines and infrastructure

Preferred Skills & Experience:

• Experience in manufacturing, aerospace, defense, or hardware-intensive industries
• Background integrating data from PLM systems (Teamcenter, Windchill), ERP systems (NetSuite, SAP), and MES/manufacturing execution systems
• Hands-on experience with MLOps tools, feature stores, and machine learning data pipelines
• Knowledge of DevOps/DataOps practices including CI/CD, infrastructure as code (Terraform, CloudFormation), and containerization (Docker, Kubernetes)
• Experience with real-time streaming architectures and event-driven systems
• Familiarity with vector databases, knowledge graphs, and AI/LLM data architectures
• Understanding of dimensional modeling, data vault methodology, and modern data architecture patterns
• Contributions to open-source data engineering projects
• Experience with digital twin architectures or simulation data management
• Strong understanding of data security, compliance, and governance in regulated industries
• Excellent communication skills with the ability to translate complex technical concepts to non-technical stakeholders
• Proven ability to balance strategic vision with tactical execution in fast-paced startup environments
• Track record of extreme ownership—proactively identifying problems and driving solutions to completion

Additional Requirements:

• Ability to travel up to 10% of the time to other Vast facilities or vendor sites
• Willingness to work extended hours or weekends to support critical mission milestones and production launches
• Must be a U.S. citizen, lawful permanent resident of the U.S., protected individual as defined by 8 U.S.C. 1324b(a)(3), or eligible to obtain required authorizations from the U.S. Department of State (export control requirement)

Pay Range:

• Staff Data Architect: $155,800- $221,160

ABOUT THE ROLE

We are looking for a talented Sr. GenAI Engineer who sits at the intersection of knowledge engineering, agentic AI, and data intelligence. In this role you will design and operate AI agents that traverse, reason over, and enrich large-scale knowledge graphs — then extend that context dynamically using live data sources such as the web, enterprise APIs, and structured databases.

The ideal candidate is deeply comfortable with graph data models, LLM orchestration frameworks, and retrieval-augmented pipelines. Bonus points if you have experience working in trade-craft or intelligence-adjacent environments where provenance, precision, and adversarial robustness are non-negotiable.

KEY RESPONSIBILITIES

Knowledge Graph Engineering

• Design, build and maintain large-scale property graphs and RDF triplestores (Neo4j, Amazon Neptune, Stardog, or equivalent).
• Develop and govern ontologies, taxonomies, and entity-relationship schemas that reflect real-world domain semantics.
• Implement graph ingestion pipelines that extract, transform, and link entities from structured, semi-structured, and unstructured data.
• Optimise graph traversal queries (Cypher, SPARQL, Gremlin) for sub-second response at production scale.
• Train and deploy graph neural networks (GNNs) for node classification, link prediction, and subgraph retrieval - Maintain model retraining workflows triggered by graph drift or coverage degradation.

Agentic AI Systems

• Architect and implement autonomous agents that plan multi-step reasoning chains over knowledge graph data using LLMs (GPT-4o, Claude, Gemini, or open-source equivalents).
• Build graph-aware Retrieval-Augmented Generation (RAG) pipelines that blend structured graph context with unstructured document retrieval.
• Design tool-use and function-calling layers so agents can query live data sources — web search, REST/GraphQL APIs, relational databases — to extend or verify graph knowledge.
• Implement agent memory, reflection, and self-correction loops to improve reliability over multi-hop tasks.

Context Enrichment & Data Fusion

• Integrate web scraping, news feeds, and open-source intelligence (OSINT) sources to keep the knowledge graph current.
• Build entity resolution and deduplication components that merge data from heterogeneous sources into a consistent graph.
• Develop confidence-scoring and provenance-tracking mechanisms so downstream consumers understand the reliability of any piece of context.

MLOps & Production Readiness

• Package agents as scalable microservices; instruments with observability tooling (tracing, latency, token cost).
• Collaborate with platform engineers to deploy workloads on cloud-native infrastructure (AWS / GCP / Azure).
• Maintain evaluation harnesses that measure agent accuracy, hallucination rate, and graph coverage over time.

REQUIRED SKILLS & EXPERIENCE

• 5 + years of professional software engineering with strong Python (or Java / Kotlin) proficiency.
• Hands-on production experience with at least one major graph database — Neo4j, Amazon Neptune, TigerGraph, or comparable.
• Demonstrated knowledge of graph query languages like Cypher, SPARQL, or Gremlin — at production query complexity.
• Direct experience building LLM-powered agents or pipelines using frameworks such as LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, or Semantic Kernel.
• Solid understanding of RAG architectures: chunking strategies, vector stores (Pinecone, Weaviate, pgvector), hybrid retrieval, and re-ranking.
• Familiarity with prompt engineering, few-shot learning, and LLM evaluation techniques.
• Experience integrating external data sources via APIs, web scraping (Playwright / Scrapy), or streaming pipelines (Kafka / Kinesis).
• Working knowledge of containerisation (Docker, Kubernetes) and CI/CD pipelines.
• Familiarity with graph export formats - at least one GraphML, RDF/OWL, or JSON-LDExperience integrating GNN-derived features into vector stores or RAG pipelines

PREFERRED QUALIFICATIONS

• Advanced degree (MS / PhD) in Computer Science, Information Science, Computational Linguistics, or a related field.
• Experience in intelligence, defence, or trade-craft environments — working with OSINT, link analysis, entity disambiguation, or signals intelligence data.
• Understanding of access-control models for sensitive graph data (need-to-know, compartmentalisation, provenance labelling).
• Familiarity with knowledge representation standards like OWL, SHACL, RDF-star, JSON-LD, W3C PROV.
• Experience with fine-tuning or instruction-tuning open-source LLMs (Llama, Mistral, Falcon) for domain-specific tasks.
• Background in network-analysis algorithms: centrality, community detection, path-finding, anomaly detection on graphs.
• Contributions to open-source graph or GenAI projects; published research or technical blog presence.
• Active or adjudicatable security clearance (Secret or above) — strongly preferred for trade-craft assignments.

The Role

We are looking for an elite engineer who builds like a hacker and communicates like a consultant. You will not just be building models in a notebook; you will be deployed to the front lines to build, architect, and ship end-to-end Agentic AI products directly for our most strategic customers.

This is a high-stakes role. You will face "tricky" customers who have vague requirements and high expectations. Your job is to translate their chaos into technical order, write production-grade code, and deploy autonomous agents that solve their actual business problems.

What You Will Do

• Build & Deploy (60%):

Design and ship full-stack AI applications. You own the pipeline from data ingestion to the LLM inference layer to the frontend interface.

Architect autonomous agentic workflows (using frameworks like LangChain, LangGraph, or custom implementations) that can reason, plan, and execute complex tasks.

Write robust, clean, and highly testable code (Python/Typescript). "It works on my machine" is not acceptable; it must work in the customer's restricted cloud environment.

Handle DevOps and MLOps constraints: Dockerizing agents, managing Kubernetes clusters, and optimizing inference latency.

• Customer Engineering (40%):

Serve as the technical face of the company for our most demanding accounts.

Navigate complex stakeholder environments. You will push back on unrealistic demands, clarify ambiguous requirements, and steer "tricky" customers toward technically feasible solutions without breaking rapport.

Translate technical constraints into business value for non-technical executives.

Generate high-quality data for our customers and ability to review the data generated by other experts. This is one of the most critical aspects of the role.

The "Must-Haves"

• Experience: 8–12 years of total engineering experience. You started as a strong Software Engineer and evolved into an AI specialist.
• Education: B.S./M.S. in Computer Science, Math, or Physics from a top-tier institution (IIT, MIT, Stanford, Harvard, Berkeley, CMU, or similar global top-ranking universities).
• Deep AI Fluency: You aren't just calling APIs. You understand RAG implementation nuances, vector database optimization, fine-tuning (LoRA/PEFT), and the architecture of agentic loops.
• Production Engineering: Experience with end-to-end deployment (AWS/GCP/Azure, CI/CD pipelines, Terraform, Docker).

The "Why You" (Soft Skills)

• Thick Skin & High EQ: You don't get flustered when a customer changes their mind for the third time in a week. You can hold your ground with a CTO and explain "why not" to a CEO.
• Extreme Attention to Detail: You catch the edge cases that others miss. You obsess over error handling, retry logic in agents, and data privacy.
• Grit: You enjoy the "messy" work of integrating legacy enterprise data just as much as prompting the latest model.

What will set you apart

• Public Code: A visible GitHub footprint is very helpful. We want to see your side projects, your contributions to open source, or your experiments with LLMs. Please include your GitHub link in your application.

Tech Stack

• Languages: Python (Expert).
• AI/Data (Expertise in all is not required): PyTorch, LangChain, LlamaIndex, Pinecone/Weaviate, OpenAI/Anthropic APIs, HuggingFace.
• Infra (Expertise in all is not required): Kubernetes, Terraform, AWS/GCP.

Compensation

232k-260k base + variable + equity

 Who We Are

2K is headquartered in Novato, California and is a wholly owned label of Take-Two Interactive Software, Inc. (NASDAQ: TTWO). Founded in 2005, 2K Games is a global video game company, publishing titles developed by some of the most influential game development studios in the world. Our studios responsible for developing 2K’s portfolio of world-class games across multiple platforms, include Visual Concepts, Firaxis, Hangar 13, CatDaddy, Cloud Chamber, 31st Union, HB Studios, and 2K SportsLab. Our portfolio of titles is expanding due to our global strategic plan, building and acquiring exciting studios whose content continues to inspire all of us! 2K publishes titles in today’s most popular gaming genres, including sports, shooters, action, role-playing, strategy, casual, and family entertainment.

Our team of engineers, marketers, artists, writers, data scientists, producers, thinkers and doers, are the professional publishing stewards of 2K’s portfolio currently includes several AAA, sports and entertainment brands, including global powerhouse NBA®️ 2K,  renowned BioShock®️, Borderlands®️, Mafia, Sid Meier’s Civilization®️ and XCOM®️ brands; popular WWE®️ 2K and WWE®️ SuperCard franchises, TopSpin 2K25, as well as the critically and commercially acclaimed PGA TOUR®️ 2K.

At 2K, we pride ourselves on creating an inclusive work environment, which means encouraging our teams to Come as You Are and do your best work! We encourage ALL applicants to explore our global positions, even if they don’t meet every requirement for the role.  If you're interested in the job and think you have what it takes to work at 2K, we encourage you to apply!

 What You Will Do

 2K Games is building a world-class data ecosystem to power real-time analytics, machine learning, and game intelligence across all our franchises. We are looking for a Senior Product Manager, Data Science to drive the vision, strategy, and execution of our data science strategies and products, partnering with game studios and analytics teams to enable enhanced player experiences.

This role requires a blend of strategic development, product management expertise, and technical understanding of data science and machine learning (ML) methodologies. The ideal candidate will seamlessly integrate data-driven strategies with player-centric goals, ensuring AI and analytics improve game design, live operations, and business intelligence.

Strategic Vision & Product Leadership

• Develop and implement the product strategy for 2K’s central data science platform, partnering with Data Scientists across our gaming business units to drive predictive analytics, player personalization, and real-time decision-making;
• Collaborate with the Director of Product, 2K Data to align the data engineering roadmap with customer objectives and player-centric outcomes;
• Define and prioritize a high-impact data science roadmap to lead a team of central data scientists in driving a variety of use cases such as player retention modeling, dynamic content generation, and real-time fraud detection. 

Data Science Platform Development

• Lead the development of reusable and extensible machine learning models, feature engineering pipelines, and ML-driven insights using tools like Databricks, MLflow, and SageMaker;
• Ensure data science initiatives integrate seamlessly with the real-time data infrastructure, supporting streaming analytics, AI-powered LiveOps, and dynamic matchmaking systems;
• Establish and maintain self-service tools that enable game teams to experiment with and deploy AI models independently;
• Support the build and enhancement of the central ML feature store;
• Monitor and optimize model performance in production environments, using MLOps standard methodologies;
• Implement governance frameworks to ensure AI models adhere responsibly to data privacy, security, and compliance standards (e.g., CCPA, GDPR). 

Cross-Functional Collaboration

• Work closely with data scientists across game studios and business units to understand their data science needs and to provide data-driven solutions at the pace they need them;
• Act as a strategic liaison between data scientists and product teams, ensuring model outputs are actionable, reliable, and aligned with business goals;
• Promote a culture of experimentation and rapid iteration, supporting game studios with A/B testing frameworks, predictive analytics, and personalization engines.
• Find opportunities to enhance game design and player engagement through advanced analytics and machine learning.

 What You Need

• 7+ years of experience in product management with a focus on data science, analytics, or ML-driven products
• 5+ years of experience in gaming, technology, or entertainment industries;
• Strong understanding of data science methodologies, such as predictive modeling, natural language processing, and computer vision;
• Experience with data science tools and platforms, such as Python, SQL, Databricks,TensorFlow, and R;
• Customer-focused mentality with focus on the Why and What without getting lost in the What and the How;
• Consistent track record of delivering data science products and features from ideation to production, with measurable business impact;
• Ability to translate complex data insights into clear product strategies and business opportunities;
• Knowledge of real-time analytics platforms, streaming data processing, and feature store management;
• Familiarity with data governance frameworks, model risk management, and MLOps standard processes preferred;
• Advanced degree or equivalent experience in Data Science, Machine Learning, Computer Science, or a related field preferred.

The pay range for this position in California at the start of employment is expected to be between $109,800 and $162,480 per Year. However, base pay offered is based on market location, and may vary further depending on individualized factors for job candidates, such as job-related knowledge, skills, experience, and other objective business considerations. Subject to those same considerations, the total compensation package for this position may also include other elements, including a bonus and/or equity awards and eligibility to participate in our 401(K) plan and Employee Stock Purchase Program. Regular, full-time employees are also eligible for a range of benefits at the Company, including: medical, dental, vision, and basic life insurance coverage; 14 paid holidays per calendar year; paid vacation time per calendar year (ranging from 15 to 25 days) or eligibility to participate in the Company’s discretionary time off program; up to 10 paid sick days per calendar year; paid parental and compassionate leave; wellbeing programs for mental health and other wellness support; family planning support through Maven; commuter benefits; and reimbursements for fitness-related expenses.

As an equal opportunity employer, we are committed to ensuring that qualified individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform their essential job functions, and to receive other benefits and privileges of employment. Please contact us if you need reasonable accommodation.

Please note that 2K Games and its studios never uses instant messaging apps or personal email accounts to contact prospective employees or conduct interviews and when emailing, only use 2K.com accounts. 

#LI-Onsite

Natural Language Processing (NLP) is central to enabling massive-scale communication, creation, and safety across the Roblox platform. This role offers the unique opportunity to build and deploy cutting-edge NLP, speech, and generative AI models that operate at an unprecedented scale, impacting hundreds of millions of daily users.

You will solve an extremely diverse range of high-scale language-related problems—from real-time moderation of voice and text to automatically localizing experiences and empowering users through LLM-driven creation tools. We combine cutting-edge research with large-scale engineering to bridge experimentation and production, designing algorithms that shape the next generation of language services for our immersive, user-generated content platform.

Teams Hiring for This Role

• Safety AI Systems: Dedicated to building end-to-end ML systems for maintaining civility and safety across the platform, operating at massive scale. This includes:
  • Real-time Moderation: Building world-class NLP and speech models for real-time moderation of voice and text (processing over 6 billion messages daily) and advanced interventions that measurably improve user civility.
  • Critical Harms & Advanced Detection: Developing specialized LLM agents, behavioral analysis, and graph systems for detecting and preventing rare, high-risk scenarios (e.g., child safety, terrorism), requiring adversarial thinking and multi-step reasoning.
  • Safety Data Quality: Ensuring all Safety AI systems are robust by managing the core data infrastructure, MLOps, and Active Learning initiatives for continuous model improvement.

You Will

• Design and implement deep learning-based NLP and speech solutions that address problems across Roblox, from creation to safety.
• Develop advanced models, including Large Language Models (LLMs), machine translation, and generative AI, for user interactions, content creation, and moderation.
• Have the independence and end-to-end responsibility to develop NLP/ML-based services that are scalable and resilient.
• Be a technical bar-raiser for cutting-edge ML technology, high code quality, and architectural designs.
• Work backward from user and product needs to deliver ML solutions that drive engagement, safety, and ecosystem growth.

You Have

• Possessing or pursuing a Ph.D. in Computer Science, Computer Engineering, Mathematics, Statistics, or a related technical field, with a thesis aligned to Roblox’s research areas.
• Expertise in one or more areas: NLP, Speech Models, Large Language Models, Machine Translation, or Generative AI (including diffusion models).
• Experience with transformer-based model design, training, serving, and product integration.
• A strong research track record, evidenced by multiple publications and presentations in top-tier, peer-reviewed venues (e.g., ACL, EMNLP, Interspeech, ICML, NeurIPS).
• Proficiency in one or more programming languages (e.g., Python, C++, Go, Java) and experience building and optimizing large-scale systems.

You may redact age, date of birth, and dates of attendance/graduation from your resume if you prefer.

As you apply, you can find more information about our process by signing up for Speak_. You'll gain access to our practice assessment, comprehensive guides, FAQs, and modules designed to help you ace the hiring process.

The AI Platform team is building the unified platform that enables machine learning at a massive scale across all products and services. As a Senior Engineering Manager for the AI Platform, Data team, you will lead the development of the high-leverage data platform used by AI engineers, machine learning scientists, and product developers. You will be responsible for defining the long-term vision, strategy, and execution for the data foundation that powers the next generation of AI-driven products. This includes work on cutting-edge technologies like vector databases and knowledge graphs to enhance our data capabilities for advanced AI use cases.

You Will:

• Lead and grow a high-performing team of software engineers, fostering a culture of ownership, collaboration, and engineering excellence.
• Define the roadmap, strategy, and execution plan for the GenAI Platform which handles LLM and agent infrastructure.
• Collaborate with AI engineering, machine learning science, and product teams to understand their data needs and deliver platform capabilities that accelerate development and deployment of traditional AI and Generative models. 
• Drive architectural decisions and set technical standards for reliability, scalability, and performance
• Oversee the end-to-end development life cycle, ensuring timely delivery of high-quality, robust data solutions.
• Mentor and coach team members, providing continuous feedback and career development guidance.
• Manage cross-functional projects and communicate progress, risks, and challenges to leadership and stakeholders.

You Are:

• A seasoned engineering leader with 5+ years of management experience and 8+ years of experience building large-scale, high-performance data platforms, machine learning infrastructure, or distributed systems.
• Experienced in hiring, managing, and mentoring senior software engineers and technical leaders.
• Deeply knowledgeable about data technologies (e.g., Spark, Hadoop, Flink, Kafka, distributed databases, data warehousing) and best practices for large-scale data processing and storage.
• Skilled at driving technical strategy and roadmap planning in ambiguous and fast-paced environments.
• Able to establish strong, collaborative relationships with cross-functional partners and senior leadership.
• Passionate about fostering a positive, inclusive, and high-energy engineering culture.
• Knowledgeable in the machine learning life cycle (MLOps) and the data requirements for training, serving, and monitoring models.
• A strong communicator, both written and verbal, capable of articulating complex technical concepts to a diverse audience.

Preferred Qualifications

• A BS, MS, or PhD in Computer Science, or a related technical field.
• Experience with cloud-based data solutions (e.g., AWS, GCP, Azure).
• Experience with real-time data processing and stream computing technologies.

The Team: 

The Machine Learning & Simulations Platform (MLSP) team builds and operates the core infrastructure that powers ML model training, inference, and marketplace simulation at Upstart. Our platform is foundational to the company’s success—every underwriting, fraud, conversion, and verification model runs here. We also provide the simulation capabilities that help teams experiment safely and assess business impact without requiring costly live experimentation.

We are on a mission to reimagine our infrastructure to support the growing complexity of our ML models, the demand for low-latency inference, and the accuracy needed to simulate the dynamics of our borrower-lender marketplace at scale. The team partners closely with Engineering, ML, Product, and Finance  to accelerate innovation while safeguarding performance and integrity.

As a Principal Software Engineer focused on Machine Learning Simulations at Upstart, you will be responsible for building an MLOps platform to support machine learning model inference, process automation, model deployment, and observability. Machine Learning is critical to Upstart’s core business, and our greatest competitive advantage lies in the fact that we’re able to innovate on our AI engine quickly. You will also help build a  marketplace simulation platform to support rapid innovation across ML and Finance teams.

How you’ll make an impact

• Build, maintain, and optimize Upstart’s next-generation machine learning and simulation platform, enabling increased scale, performance, and confidence in decisioning
• Develop high-quality software applications that enable machine learning models to be applied to the ever-evolving needs of the business
• Enable the  modernization of our serving infrastructure, reducing inference latency to just a few seconds for our most complex models
• Design and contribute to  our simulation systems to more accurately reflect production environments, reducing simulation cost and enabling broader usage across teams
• Communicate closely with cross-functional partners from  ML, Engineering, Product, and Data Engineering  teams, keeping all stakeholders informed
• Mentor engineers across the team, sharing expertise on distributed systems, MLOps, and scalable architecture

Minimum Qualifications  

• Bachelor’s degree in Computer Science, Engineering, or Mathematics, or a related field (or its equivalent) + 8 years of experience
• Experience building or contributing to platforms or systems that support machine learning model simulation
• Experience building self-serve or configuration-driven tooling for internal stakeholders
• Experience building and maintaining backend software services and APIs
• Proficiency with some or more of the following: Python, Kotlin, Databricks, and AWS
• Exhibits a growth mindset - you’re not afraid to pick up new technologies that are best for the task, and learn from others.
• Ability to quickly comprehend and reiterate complex requirements from product or engineering leadership and translate those to both technical and non-technical stakeholders
• Track record of successfully mentoring and developing other engineers around you while seeking out and appreciating constructive feedback

Preferred Qualifications

• Familiarity with model serving technologies like Ray, and experimentation frameworks
• Proficiency with Flask, FastAPI, Metaflow, MLflow, gRPC, Kafka, Spark/PySpark, ETL/ELT, Redshift (or similar)
• Excellent quantitative reasoning skills with interest in working at the intersection of engineering and machine learning
• Strong sense of ownership and accountability for the quality and timely delivery of work
• Proven ability to effectively analyze and solve complex problems
• Excellent written and verbal communication skills with stakeholders, peers and product owners
• Ability to thrive both in self-directed work environments and in collaborative settings, contributing positively to team dynamic

Position location This role is available in the following locations: Remote-US

Time zone requirements The team operates on the East/West coast time zones.

Travel requirements As a digital first company, the majority of your work can be accomplished remotely. The majority of our employees can live and work anywhere in the U.S but are encouraged to to still spend high quality time in-person collaborating via regular onsites. The in-person sessions’ cadence varies depending on the team and role; most teams meet once or twice per quarter for 2-4 consecutive days at a time.

#LI-REMOTE

#LI-MidSenior

Overview

As a Senior Machine Learning Engineer II on the Ads Response Prediction team, you will lead the design and development of core ML models that power Instacart’s ads ecosystem. This is a research-leaning role focused on theoretical problem formulation, training methodology, and model quality rather than infrastructure or full-stack engineering. You will tackle fundamental challenges in pCTR modeling such as mitigating selection bias, position bias, and optimizer’s curse in training data, improving model calibration across surfaces and domains, and advancing our multi-task learning and sequence modeling capabilities. You will also have the opportunity to shape our next-generation foundation model approach for ads ranking and contribute to cutting-edge retrieval systems like TIGER (Transformer Index for Generative Recommenders), Semantic ID and domain language models.

The Ads Response Prediction team owns all systems, algorithms and ML models to ensure a relevant and engaging Ads experience to customers of all the platforms powered by Instacart. This includes search and exploration retrieval systems, sequential modeling and generative retrieval systems for next interaction recommendations, LLM integrations, relevance models, pCTR models, bidding models and incrementality models. The team optimizes for an efficient marketplace to ensure delightful customer shopping experience, desirable advertiser business outcome and Instacart Ads revenue.

The team has strong ML infrastructure and MLOps support, including Delta/DBT-Spark data pipelines, Ray-based distributed training, and automated model deployment. This means you can focus your energy on advancing modeling science rather than building infrastructure.

About the Job

• Lead research and development of pCTR and conversion prediction models, with a focus on improving calibration, reducing training data biases (selection bias, position bias, optimizer’s curse), and advancing model accuracy across Instacart’s ads surfaces.
• Design and implement debiasing techniques such as Mixed Negative Sampling (MNS), Inverse Propensity Weighting (IPW), counterfactual risk minimization, and calibration methods (Platt scaling, isotonic regression) to address systematic prediction biases.
• Contribute to the next-generation Multi-Domain Multi-Task (MDMT) model architecture, incorporating innovations like Mixture-of-Experts (MoE), Transformer layers for sequential user behavior, and LoRA adaptors for scalable domain fine-tuning.
• Drive sequence modeling initiatives including the TIGER generative retrieval system and Semantic ID representation learning, expanding their application across ads surfaces such as Product Details, Search and other placements.
• Collaborate with the broader ML community in the company on the path toward Foundation Models using autoregressive user behavior prediction.
• Formulate and scope ambiguous modeling problems from first principles. Translate business observations (e.g., overcalibration patterns, cold-start underperformance) into well-defined ML research directions with clear evaluation criteria.
• Publish and present findings internally. Contribute to the team’s culture of technical rigor through design reviews, paper sharing, and experiment retrospectives.

About You

Minimum Qualifications

• PhD/Master in machine learning, statistics, computer science, information retrieval, or a closely related quantitative field.
• 6+ years of combined academic and industry experience (including PhD research) applying ML to ranking, recommendation, or prediction problems at scale.
• Deep understanding of CTR/conversion prediction modeling, including familiarity with architectures such as Deep & Wide, DeepFM, DCN, and multi-task learning formulations.
• Strong foundation in causal inference, counterfactual reasoning, and training data bias mitigation. Ability to reason about selection bias, position bias, and propensity-based correction methods.
• Proficiency in Python and deep learning frameworks (PyTorch, Tensorflow, JAX). Fluency in data manipulation tools (SQL, Spark, Pandas).
• Track record of formulating ambiguous problems into well-scoped ML research directions and delivering results through rigorous experimentation.
• Strong written and verbal communication skills. Ability to explain complex modeling decisions to cross-functional stakeholders including product managers and data scientists.

Preferred Qualifications

• Experience in ads ranking or auction-based systems (pCTR, bid optimization, ROAS feedback loops, marketplace dynamics).
• Hands-on experience with autoregressive sequence models for user behavior prediction, generative retrieval, or transformer-based ranking architectures.
• Familiarity with learned representations such as Semantic IDs, product embeddings, or other approaches to reducing feature cardinality and cold-start challenges.
• Experience with transfer learning or domain adaptation techniques (e.g., LoRA, adapter-based fine-tuning) applied to recommendation or ranking models.
• Publication record in top-tier venues (KDD, WWW, RecSys, NeurIPS, ICML, SIGIR, or similar).
• Experience mentoring junior engineers or shaping technical direction for a modeling team.
• Familiarity with LLM-driven approaches to recommendation, including prompt-based personalization and AI-assisted model development (AutoML).

The role of a Senior Data Scientist at Dataiku is quite unique. Our Data Scientists not only code up solutions to real-world problems but also participate in client-facing endeavors throughout the customer journey. This includes supporting their discovery of the platform, helping integrate Dataiku with other tools and technologies, some user training, and co-developing data science projects from design to deployment.Just as the non-technical skills are important, so too are the technical. Our Data Scientists work on the Dataiku platform every day. Aside from the visual tools, our team uses mostly Python and SQL, with occasional work in other languages (e.g., R, Pyspark, JavaScript, etc.). An ideal candidate is excited to learn complex new technologies and modeling techniques while being able to explain their work to other data scientists and clients.

Key Areas of Responsibility (What You’ll Do)

• Scope and co-develop production-level data science projects with our customers across different industries and use cases
• Help users discover and master the Dataiku platform via user training, office hours, and ongoing consultative support
• Provide strategic input to the customer and account teams that help make our customers successful.
• Provide data science expertise both to customers and internally to Dataiku’s sales and marketing teams
• Lead technical data science projects pre-sales scoping and design appealing proposals
• Flag technical and non-technical account risks (onboarding issues, performance pitfalls, timeline slippage)
• Develop custom Python-based “plugins” in collaboration with Solutions, R&D, and Product teams, to enhance Dataiku’s functionality
•  Lead Data Scientist engagements: You will coordinate agile sprints, prioritize tasks, estimate effort, do backlog grooming
• Run demo booth/tech talk duties at company public events (e.g. Everyday AI)
• Lead Junior Data Scientist technical interview
• Contribute to 2 internal assets (internal best practice or external blog post/project on the public gallery) per year

Experience (What We’re Looking For):

• Curiosity and a desire to learn new topics and skills
• Empathy for others and an eagerness to share your knowledge and expertise with your colleagues, Dataiku’s customers, and the general public
• The ability to clearly explain complex topics to technical as well as non-technical audiences
• Over 5 years of experience with Python and SQL
• Over 5 years of experience with building ML models and using ML tools (e.g., sklearn)
• Experience with LLMs
• Experience with data visualization and building web apps with Python frameworks (Dash, Streamlit)
• Understanding of underlying data systems such as Cloud architectures and SQL
• Bachelor’s or Master’s program focused on: Statistics, Computer Science, or a related field
• Location: Must be located within the CST, MST, and PST time zones

Bonus points for any of these:

• Experience using Dataiku DSS
• Experience with Consulting and/or Customer-facing Data Science roles
• Experience in the Manufacture & Defense sector
• Experience with Data Engineering or MLOps
• Experience developing WebApps in Javascript
• Experience building APIs
• Passion for teaching or public speaking

As a Senior Software Engineer, you will be tasked with leading the architectural design and technical strategy for high-performance systems that process massive volumes of telemetry data while reducing costs and improving insights for enterprise customers. You will drive technical excellence and lead complex engineering initiatives at scale for the Observo.ai team, our cutting-edge AI-driven data pipeline optimization platform.

What Will You Do?

Primary responsibilities include:

• Lead the architectural design and technical roadmap for scalable, high-performance data processing pipelines capable of handling petabyte-scale telemetry data (logs, metrics, traces).
• Drive the development and optimization of ML-driven data routing and transformation engines to reduce customer data volumes by 80%+ while architecting real-time analytics systems using advanced machine learning and LLMs.
• Design cloud-native microservices and APIs that integrate seamlessly with major observability platforms (Splunk, Elastic, Datadog) while establishing robust monitoring, alerting, and observability solutions.
• Lead cross-functional technical initiatives and decision-making forums, collaborating with Product, Data Science, and DevOps teams to translate strategic vision into technical solutions and company-wide standards.
• Provide technical leadership and mentorship to senior and junior engineers, driving system performance and reliability optimization while establishing engineering best practices and culture.

What Skills and Knowledge Will You Bring?

Ideal candidates will have:

• 5+ years of software engineering experience focused on distributed systems, data engineering, or ML infrastructure with expert-level proficiency in Go, Rust, or Java.
• Extensive experience with cloud platforms (AWS, GCP, Azure), container orchestration (Kubernetes), and a proven track record leading and scaling data pipelines using Kafka, Spark, or Flink.
• Deep expertise in database technologies (SQL and NoSQL) and advanced experience with machine learning frameworks (TensorFlow, PyTorch) and MLOps practices for production ML systems.
• Expert knowledge of observability tools and standards, including Prometheus, Grafana, ELK stack, OpenTelemetry, and Parquet, alongside extensive experience with Infrastructure as Code (Terraform).
• Strong leadership and technical communication skills, with a track record of mentoring engineers, planning technical strategy, and driving decisions across multiple teams and stakeholders.

Why SentinelOne?

AI is redefining how the world operates and rewriting the rules of security in real time, and SentinelOne was built for this moment. From day one, we architected an AI-native platform designed to operate at machine speed, not as an add-on to legacy systems but as the foundation itself. If you want to build where innovation and impact move together, this is that place.

We invest in our Sentinels with comprehensive, competitive benefits designed to support you and your family:

• Medical, Vision, Dental, 401(k), Commuter, Health and Dependent FSA
• Unlimited PTO
• Industry-leading gender-neutral parental leave
• Paid company holidays
• Paid sick time
• Employee stock purchase program
• Disability and life insurance
• Employee assistance program
• Gym membership reimbursement
• Cell phone reimbursement
• Numerous company-sponsored events, including regular happy hours and team-building events

WHO WE ARE 

Zeta Global (NYSE: ZETA) is the AI-Powered Marketing Cloud that leverages advanced artificial intelligence (AI) and trillions of consumer signals to make it easier for marketers to acquire, grow, and retain customers more efficiently. Through the Zeta Marketing Platform (ZMP), our vision is to make sophisticated marketing simple by unifying identity, intelligence, and omnichannel activation into a single platform – powered by one of the industry’s largest proprietary databases and AI. Our enterprise customers across multiple verticals are empowered to personalize experiences with consumers at an individual level across every channel, delivering better results for marketing programs. Zeta was founded in 2007 by David A. Steinberg and John Sculley and is headquartered in New York City with offices around the world. To learn more, go to www.zetaglobal.com.

THE ROLE

At Zeta Global, we are spearheading innovations in data-driven marketing, leveraging advanced machine learning and AI to deliver unparalleled insights and performance. We are seeking a visionary Senior Product Manager to lead our Machine Learning Operations initiatives, focusing on developing retail-specific models and a user-friendly Model Workbench that empowers marketers to harness the power of Machine Learning without the need for deep technical expertise. 

Job Description:

• Machine Learning Operations Leadership: Spearhead the development and maintenance of robust ML Ops processes to streamline model deployment, monitoring, and management. 
• Retail Model Innovation: Design and refine machine learning models tailored to the retail industry, enhancing our predictive capabilities and market understanding. 
• Model Workbench Development: Lead the creation of a Model Workbench platform that enables marketers to easily interact with and utilize machine learning models, incorporating user-friendly interfaces and tools. 
• Customer-Centric Product Development: Ensure all product development is guided by a deep understanding of customer needs and market demands, delivering solutions that are both intuitive and powerful. 
• Agentic Infrastructure Integration: Work towards integrating traditional ML with advanced generative models, focusing on explainability, actionable recommendations, and recursive training based on user interactions.

Requirements:

• Experience in Product Management: Proven track record of successful product management, ideally in a technology-focused environment. 
• Expertise in Machine Learning: Strong understanding of ML Ops practices, with experience in developing and managing Machine Learning models, particularly for retail applications. 
• Technical Proficiency: Comfortable working with both traditional and generative machine learning technologies, with a keen insight into model explainability and user interaction. 
• Strategic Thinking: Ability to think strategically about product development, integrating customer insights and market trends into actionable product features. 
• Communication Skills: Excellent communication skills with the ability to collaborate effectively across cross-functional teams and articulate product visions clearly to both technical and non-technical stakeholders.
• Background in Retail Analytics: Direct experience or a strong understanding of the retail industry's data analytics needs. 
• Familiarity with Agentic Infrastructure: Knowledge of combining traditional ML models with generative models to enhance user interaction and model training. 

BENEFITS & PERKS 

• Unlimited PTO 
• Excellent medical, dental, and vision coverage 
• Employee Equity and Stock Purchase Plan 
• Employee Discounts, Virtual Wellness Classes, and Pet Insurance 

And more!!  

COMPENSATION RANGE 

The compensation range for this role is $150,000 - $190,000 depending on location and experience.  

PEOPLE & CULTURE AT ZETA 

Zeta considers applicants for employment without regard to, and does not discriminate on the basis of an individual’s sex, race, color, religion, age, disability, status as a veteran, or national or ethnic origin; nor does Zeta discriminate on the basis of sexual orientation, gender identity or expression.  

We’re committed to building a workplace culture of trust and belonging, so everyone feels invited to bring their whole selves to work. We provide a forum for employees to celebrate, support and advocate for one another. Learn more about our commitment to diversity, equity and inclusion here: https://zetaglobal.com/blog/a-look-into-zetas-ergs/ 

ZETA IN THE NEWS! 

https://zetaglobal.com/press/?cat=press-releases 

#LI-ND1

#LI-Remote

Mission Summary:

At Motional, we're transforming how autonomous vehicles discover critical intelligence hidden within petabytes of multimodal sensor data. Our next-generation autonomous driving stack depends on finding the rare edge cases, long-tail scenarios, and model errors that matter most. Omnitag, our ML-powered multimodal data mining framework, is the engine that powers this discovery.

As a Staff Machine Learning Engineer, you will serve as a technical leader defining the roadmap and architecture for the machine learning systems that power our data discovery and model improvement lifecycles. Rather than focusing on a single specialized domain, you will leverage your broad ML expertise to architect massive, scalable systems, from multimodal representation learning and active learning loops to hyper-efficient production inference. You will own system-level architecture, lead multi-quarter, multi-person initiatives, and partner across the engineering organization to unblock teams and influence our department-wide technical strategy. By establishing robust processes and mentoring those around you, you will ensure our ML platforms act as a reliable, mission-critical engine for the entire autonomy stack.

What You'll Do:

• Define Technical Strategy & Roadmaps: Develop and execute multi-quarter, high-impact technical roadmaps for core ML systems. Proactively inform leadership to guide reprioritization, ensuring initiatives consistently drive team-wide and department-level OKRs and KPIs.
• Architect System-Level Solutions: Own the system-level architecture for complex ML products. Design scalable frameworks for massive data mining and highly optimized, real-time inference across GPU/CPU clusters.
• Drive Cross-Functional Execution: Lead multi-person projects to completion across teams. Influence partner teams' technical roadmaps (such as Autonomy) to solve shared problems, break down silos, and build alignment.
• Elevate Engineering Excellence: Establish department-wide standards for ML system design, code quality, testing, and deployment. Deliver processes to proactively address issues and participate in org-wide incident response planning.
• Operate as a Generalist Expert: Apply a broad toolkit of ML techniques (deep learning, representation learning, active learning, generative AI) to solve complex, ambiguous problems. Unblock yourself and your team when facing unprecedented technical challenges.
• Mentor and Lead: Act as a role model and technical go-to person. Coach Senior and junior engineers, lead architectural reviews, and elevate Motional’s engineering culture through internal documentation, tech talks, and collaborative design.
  
  

What We're Looking For (Must-Haves):

• BS in Computer Science, Machine Learning, or a related field (or equivalent practical experience)
• 8+ years of hands-on ML engineering experience, with a proven track record of owning architecture, deployment, and optimization of large-scale ML systems
• Demonstrated experience working with multimodal foundation models in ML production systems, including integration, scaling, fine-tuning, or deployment of models that process multiple data modalities (e.g., camera, LiDAR, radar, text)
• Demonstrated technical leadership: defining multi-quarter roadmaps, leading multi-person initiatives, and driving department-level technical strategy
• Expert-level proficiency in Python and ML frameworks (PyTorch, TensorFlow, or JAX), backed by strong software engineering fundamentals (system design, CI/CD, containerization)
• Broad ML generalist knowledge, with practical experience spanning model training, deep learning architectures, evaluation methodologies, and production deployment at scale
• Experience deploying ML models in cloud environments (AWS, GCP, or Azure) and optimizing for latency, throughput, and hardware efficiency
• Proven ability to mentor peers, explain complex trade-offs to leadership, and drive consensus across disparate teams
  
  

Bonus Points (Nice-to-Haves):

• MS/PhD in Computer Science, Machine Learning, or a related field.
• Background in autonomous driving, robotics, or complex real-time decision-making systems.
• Experience with massive-scale ML data mining, active learning loops, and contrastive/representation learning.
• Familiarity with multimodal learning, sensor fusion, or large foundation models.
• Deep knowledge of model serving tools (TF Serving, Triton, TorchServe) and enterprise MLOps platforms.
• Demonstrated experience leading org-wide severity reviews or establishing incident response planning for mission-critical ML platforms.
  
  

We encourage a hybrid schedule with in-office time at one of our locations in Boston, Pittsburgh, or Las Vegas to support collaboration, or this role can be fully remote.

Mission Summary:

At Motional, we're transforming how autonomous vehicles discover critical intelligence hidden within petabytes of multimodal sensor data. Our next-generation autonomous driving stack depends on finding the rare edge cases, long-tail scenarios, and model errors that matter most. Omnitag, our ML-powered multimodal data mining framework, is the engine that powers this discovery.

As a Staff Machine Learning Engineer, you will serve as a technical leader defining the roadmap and architecture for the machine learning systems that power our data discovery and model improvement lifecycles. Rather than focusing on a single specialized domain, you will leverage your broad ML expertise to architect massive, scalable systems, from multimodal representation learning and active learning loops to hyper-efficient production inference. You will own system-level architecture, lead multi-quarter, multi-person initiatives, and partner across the engineering organization to unblock teams and influence our department-wide technical strategy. By establishing robust processes and mentoring those around you, you will ensure our ML platforms act as a reliable, mission-critical engine for the entire autonomy stack.

What You'll Do:

• Define Technical Strategy & Roadmaps: Develop and execute multi-quarter, high-impact technical roadmaps for core ML systems. Proactively inform leadership to guide reprioritization, ensuring initiatives consistently drive team-wide and department-level OKRs and KPIs.
• Architect System-Level Solutions: Own the system-level architecture for complex ML products. Design scalable frameworks for massive data mining and highly optimized, real-time inference across GPU/CPU clusters.
• Drive Cross-Functional Execution: Lead multi-person projects to completion across teams. Influence partner teams' technical roadmaps (such as Autonomy) to solve shared problems, break down silos, and build alignment.
• Elevate Engineering Excellence: Establish department-wide standards for ML system design, code quality, testing, and deployment. Deliver processes to proactively address issues and participate in org-wide incident response planning.
• Operate as a Generalist Expert: Apply a broad toolkit of ML techniques (deep learning, representation learning, active learning, generative AI) to solve complex, ambiguous problems. Unblock yourself and your team when facing unprecedented technical challenges.
• Mentor and Lead: Act as a role model and technical go-to person. Coach Senior and junior engineers, lead architectural reviews, and elevate Motional’s engineering culture through internal documentation, tech talks, and collaborative design.
  
  

What We're Looking For (Must-Haves):

• BS in Computer Science, Machine Learning, or a related field (or equivalent practical experience)
• 8+ years of hands-on ML engineering experience, with a proven track record of owning architecture, deployment, and optimization of large-scale ML systems
• Demonstrated experience working with multimodal foundation models in ML production systems, including integration, scaling, fine-tuning, or deployment of models that process multiple data modalities (e.g., camera, LiDAR, radar, text)
• Demonstrated technical leadership: defining multi-quarter roadmaps, leading multi-person initiatives, and driving department-level technical strategy
• Expert-level proficiency in Python and ML frameworks (PyTorch, TensorFlow, or JAX), backed by strong software engineering fundamentals (system design, CI/CD, containerization)
• Broad ML generalist knowledge, with practical experience spanning model training, deep learning architectures, evaluation methodologies, and production deployment at scale
• Experience deploying ML models in cloud environments (AWS, GCP, or Azure) and optimizing for latency, throughput, and hardware efficiency
• Proven ability to mentor peers, explain complex trade-offs to leadership, and drive consensus across disparate teams
  
  

Bonus Points (Nice-to-Haves):

• MS/PhD in Computer Science, Machine Learning, or a related field.
• Background in autonomous driving, robotics, or complex real-time decision-making systems.
• Experience with massive-scale ML data mining, active learning loops, and contrastive/representation learning.
• Familiarity with multimodal learning, sensor fusion, or large foundation models.
• Deep knowledge of model serving tools (TF Serving, Triton, TorchServe) and enterprise MLOps platforms.
• Demonstrated experience leading org-wide severity reviews or establishing incident response planning for mission-critical ML platforms.
  
  

We encourage a hybrid schedule with in-office time at one of our locations in Boston, Pittsburgh, or Las Vegas to support collaboration, or this role can be fully remote.

Mission Summary:

At Motional, we're transforming how autonomous vehicles discover critical intelligence hidden within petabytes of multimodal sensor data. Our next-generation autonomous driving stack depends on finding the rare edge cases, long-tail scenarios, and model errors that matter most. Omnitag, our ML-powered multimodal data mining framework, is the engine that powers this discovery.

As a Staff Machine Learning Engineer, you will serve as a technical leader defining the roadmap and architecture for the machine learning systems that power our data discovery and model improvement lifecycles. Rather than focusing on a single specialized domain, you will leverage your broad ML expertise to architect massive, scalable systems, from multimodal representation learning and active learning loops to hyper-efficient production inference. You will own system-level architecture, lead multi-quarter, multi-person initiatives, and partner across the engineering organization to unblock teams and influence our department-wide technical strategy. By establishing robust processes and mentoring those around you, you will ensure our ML platforms act as a reliable, mission-critical engine for the entire autonomy stack.

What You'll Do:

• Define Technical Strategy & Roadmaps: Develop and execute multi-quarter, high-impact technical roadmaps for core ML systems. Proactively inform leadership to guide reprioritization, ensuring initiatives consistently drive team-wide and department-level OKRs and KPIs.
• Architect System-Level Solutions: Own the system-level architecture for complex ML products. Design scalable frameworks for massive data mining and highly optimized, real-time inference across GPU/CPU clusters.
• Drive Cross-Functional Execution: Lead multi-person projects to completion across teams. Influence partner teams' technical roadmaps (such as Autonomy) to solve shared problems, break down silos, and build alignment.
• Elevate Engineering Excellence: Establish department-wide standards for ML system design, code quality, testing, and deployment. Deliver processes to proactively address issues and participate in org-wide incident response planning.
• Operate as a Generalist Expert: Apply a broad toolkit of ML techniques (deep learning, representation learning, active learning, generative AI) to solve complex, ambiguous problems. Unblock yourself and your team when facing unprecedented technical challenges.
• Mentor and Lead: Act as a role model and technical go-to person. Coach Senior and junior engineers, lead architectural reviews, and elevate Motional’s engineering culture through internal documentation, tech talks, and collaborative design.
  
  

What We're Looking For (Must-Haves):

• BS in Computer Science, Machine Learning, or a related field (or equivalent practical experience)
• 8+ years of hands-on ML engineering experience, with a proven track record of owning architecture, deployment, and optimization of large-scale ML systems
• Demonstrated experience working with multimodal foundation models in ML production systems, including integration, scaling, fine-tuning, or deployment of models that process multiple data modalities (e.g., camera, LiDAR, radar, text)
• Demonstrated technical leadership: defining multi-quarter roadmaps, leading multi-person initiatives, and driving department-level technical strategy
• Expert-level proficiency in Python and ML frameworks (PyTorch, TensorFlow, or JAX), backed by strong software engineering fundamentals (system design, CI/CD, containerization)
• Broad ML generalist knowledge, with practical experience spanning model training, deep learning architectures, evaluation methodologies, and production deployment at scale
• Experience deploying ML models in cloud environments (AWS, GCP, or Azure) and optimizing for latency, throughput, and hardware efficiency
• Proven ability to mentor peers, explain complex trade-offs to leadership, and drive consensus across disparate teams
  
  

Bonus Points (Nice-to-Haves):

• MS/PhD in Computer Science, Machine Learning, or a related field.
• Background in autonomous driving, robotics, or complex real-time decision-making systems.
• Experience with massive-scale ML data mining, active learning loops, and contrastive/representation learning.
• Familiarity with multimodal learning, sensor fusion, or large foundation models.
• Deep knowledge of model serving tools (TF Serving, Triton, TorchServe) and enterprise MLOps platforms.
• Demonstrated experience leading org-wide severity reviews or establishing incident response planning for mission-critical ML platforms.
  
  

We encourage a hybrid schedule with in-office time at one of our locations in Boston, Pittsburgh, or Las Vegas to support collaboration, or this role can be fully remote.

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. 

Customer experience:

Customer experience at Nebius AI Cloud involves tackling customers’ challenges and directly impacting their success by solving real-world AI and ML problems at massive GPU cloud scale. You’ll not only resolve issues, but play a key role in shaping clients’ business success by optimizing their AI solutions. Working with advanced GPUs such as H200, B200 and GB200, as well as modern ML frameworks, you’ll influence the development of the Nebius AI Cloud and gain experience at the intersection of infrastructure and AI. With minimal bureaucracy, you’ll have the freedom to innovate, take ownership and drive change. Opportunities for growth are abundant in this vibrant and supportive professional community.

The role

We are looking for a Partner Solutions Architect to serve as the technical interface between Nebius and our strategic technology partners, ranging from data platforms and MLOps vendors to AI frameworks and ISVs that build on GPU infrastructure.

This is a hands-on engineering and integration role. You will design and develop integrated solutions, build reference implementations, enable partner engineering teams, and ensure joint customers succeed with combined offerings. You will influence Nebius’ product roadmap and drive deep technical collaboration across partner ecosystems.

You’re welcome to work remotely from the United States or Canada.

Your responsibilities will include: 

• Own the technical relationship with strategic partners as the primary interface to Nebius engineering and product
• Design and architect high-impact integration solutions, delivering clear joint customer value
• Build and maintain reference implementations and production-grade integrations
• Define enterprise-ready integration patterns (networking, security, compliance, observability)
• Translate partner and customer feedback into actionable product and roadmap requirements
• Enable partner teams through technical documentation, training, and demo environments
• Provide technical leadership in joint customer deployments, architecture reviews, and complex troubleshooting

We expect you to have: 

• 7+ years in solutions architecture, partner engineering, or technical pre-sales at cloud or data platform companies
• Strong expertise in cloud infrastructure
• Hands-on experience with GPU-accelerated AI/ML workflows (training, inference, MLOps)
• Proven background in integration architecture (API design, data pipelines, security, observability)
• Experience with Python and IaC tools
• Excellent communication skills with the ability to engage technical and non-technical stakeholders

It will be an added bonus if you have: 

• Track record of building or scaling partner technical programs or ecosystems
• Experience working with or at Snowflake, Databricks, AWS, Azure, or GCP
• Background in ML lifecycle (training pipelines, fine-tuning, inference optimization, RAG
• Understanding of compliance standards: SOC 2, HIPAA, GDPR, FedRAMP

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, ranging from 225k - 315k OTE (On-Target Earnings) and equity based on your experience, skills, and location.

Join Nebius Today!

Customer experience:

Customer experience at Nebius AI Cloud involves tackling customers’ challenges and directly impacting their success by solving real-world AI and ML problems at massive GPU cloud scale. You’ll not only resolve issues, but play a key role in shaping clients’ business success by optimizing their AI solutions. Working with advanced GPUs such as H200, B200 and GB200, as well as modern ML frameworks, you’ll influence the development of the Nebius AI Cloud and gain experience at the intersection of infrastructure and AI. With minimal bureaucracy, you’ll have the freedom to innovate, take ownership and drive change. Opportunities for growth are abundant in this vibrant and supportive professional community.

The role

We are seeking a Specialist HPC Infrastructure Solutions Architect to design, build, and optimize next-generation high-performance computing (HPC) platforms for AI, simulation, and large-scale data processing workloads. The ideal candidate combines deep knowledge of cloud-native architecture, Kubernetes orchestration, networking, and HPC system design with hands-on experience implementing NVIDIA GPU-based compute environments and MLOps toolchains. This role sits at the intersection of infrastructure engineering, accelerated computing, and AI systems design, shaping the foundation for high-throughput, low-latency distributed workloads in cloud environment.

You’re welcome to work remotely from the United States or Canada.

Your responsibilities will include:

• Architect and implement scalable HPC clusters optimized for AI, simulation, and distributed training, leveraging container orchestration frameworks and schedulers (e.g., Kubernetes, Slurm).
• Design and integrate GPU-accelerated compute infrastructures featuring NVIDIA Hopper, Blackwell architectures, NVLink/NVSwitch, and InfiniBand/RoCE Interconnects.
• Deploy, and manage GPU Operator and Network Operator stacks for automated lifecycle management of GPU and high-speed networking components.
• Design and validate cloud HPC environments, focusing on low-latency, high-bandwidth networking, multi-GPU scaling, and efficient workload scheduling.
• Lead reference architectures for AI/ML model training, data pipelines, and MLOps integrations using modern observability and CI/CD tooling.
• Collaborate with hardware vendors (e.g., NVIDIA) and cloud providers to evaluate and optimize emerging HPC and GPU technologies.
• Benchmark system performance, identify bottlenecks, and tune resource utilization across compute, network, and storage tiers.
• Provide expert-level technical guidance to customers, internal teams, and partners on HPC architecture patterns, operational excellence reviews and customer engagements

We expect you to have:

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field (Ph.D. a plus)
• 3+ years of hands-on experience architecting HPC or large-scale GPU clusters.
• Expertise in Linux systems, Kubernetes, container runtimes (containers, CRI-O, Docker), and related CI/CD practices.
• Strong understanding of HPC networking protocols and RDMA stacks (InfiniBand, NVLink/NVSwitch)
• Deep understanding of storage and I/O optimization for large datasets (Ceph, Lustre, NFS, GPUDirect Storage)
• Familiarity with Terraform, Ansible, Helm, and GitOps workflows.
• Strong scripting skills in Python or Bash for automation and tool integration.
• Excellent communication and documentation skills; ability to lead design
• reviews and customer engagements

It will be an added bonus if you have:

• Proficient with NVIDIA GPU ecosystem: GPU Operator, MIG, DCGM, NCCL, Nsight, and CUDA stack management.
• Experience designing or managing AI/ML pipelines via MLflow, Kubeflow, NeMo, or similar frameworks.
• Experience with cloud-native HPC offerings (Slurm, LFS, PBS, etc.).
• Background in designing multi-tenant GPU infrastructures or AI training farms.
• Exposure to distributed ML frameworks (PyTorch DDP, DeepSpeed, Megatron).
• Knowledge of observability for HPC (Prometheus, DCGM Exporter, Grafana, NVIDIA NGC monitoring tools)
• Contribution to open-source HPC/CUDA/Kubernetes projects is a strong plus

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, ranging from 225k - 315k OTE (On-Target Earnings) and equity based on your experience, skills, and location.

Join Nebius Today!

Customer Experience:

The Customer Experience Team at Nebius AI Cloud involves tackling customers’ challenges and directly impacting their success by solving real-world AI and ML problems at massive GPU cloud scale. You’ll not only resolve issues, but play a key role in shaping clients’ business success by optimizing their AI solutions. Working with advanced GPUs such as H200, B200 and GB200, as well as modern ML frameworks, you’ll influence the development of the Nebius AI Cloud and gain experience at the intersection of infrastructure and AI. With minimal bureaucracy, you’ll have the freedom to innovate, take ownership and drive change. Opportunities for growth are abundant in this vibrant and supportive professional community.

The role

We are seeking a highly skilled and customer-focused professional to join our team as a AI/ML Specialist Solutions Architect specializing in Cloud infrastructure and MLOps. As a Cloud Solutions Architect, you will play a pivotal role in designing and implementing cutting-edge solutions for our clients, leveraging cloud technologies for ML/AI teams and becoming a trusted technical advisor for building their pipelines.

You’re welcome to work remotely from the US or Canada.

Your responsibilities will include: 

• Act as a trusted advisor to our clients, providing technical expertise and guidance throughout the engagement. Conduct PoC, workshops, presentations, and training sessions to educate clients on GPU cloud technologies and best practices.
• Collaborate with clients to understand their business requirements and develop solution architecture that align with their needs: design and document Infrastructure as code solutions, documentation and technical how-tos in collaboration with support engineers and technical writers.
• Help customers to optimize pipeline performance and scalability to ensure efficient utilization of cloud resources and services powered by Nebius AI.
• Act as a single point of expertise of customer scenarios for product, technical support, marketing teams.
• Assist to Marketing department efforts during events (Hackathons, conferences, workshops, webinars, etc.)

We expect you to have: 

• 5 - 10 + years of experience as a cloud solutions architect, system/network engineer, developer or a similar technical role with a focus on cloud computing
• Strong hands-on experience with IaC and configuration management tools (preferably Terraform/Ansible), Kubernetes, skills of writing code in Python
• Solid understanding of GPU computing practices for ML training and inference workloads, GPU software stack components, including drivers, libraries (e.g. CUDA, OpenCL)
• Excellent communication skills
• Customer-centric mindset

It will be an added bonus if you have: 

• Hands-on experience with HPC/ML orchestration frameworks (e.g. Slurm, Kubeflow)
• Hands-on experience with deep learning frameworks (e.g. TensorFlow, PyTorch)
• Solid understanding of cloud ML tools landscape from industry leaders (NVIDIA, AWS, Azure, Google) 

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, ranging from 225k - 315k OTE (On-Target Earnings) and equity based on your experience, skills, and location.

Join Nebius Today!

Summary:

Location: Remote from USA
Duration: 3 months
Start date: June 2026 
Compensation: Paid
Eligibility: Current University student (Computer Science or related field), Recent Graduate or Early Career specialist
Work authorization: permitted to work in the job’s location

Why work at Nebius
Nebius is leading a new era in cloud computing to serve the global AI economy. We create the tools and resources our customers need to solve real-world challenges and transform industries, without massive infrastructure costs or the need to build large in-house AI/ML teams. Our employees work at the cutting edge of AI cloud infrastructure alongside some of the most experienced and innovative leaders and engineers in the field.

Listed on Nasdaq, Nebius has a global footprint with R&D hubs across Europe, North America, and Israel. The team of over 1400+ employees includes more than 400 highly skilled engineers with deep expertise across hardware and software engineering, as well as an in-house AI R&D team.

The role

Customer experience at Nebius AI Cloud focuses on helping customers successfully adopt and scale AI workloads. As an intern, you’ll support this mission by learning how real-world AI systems are designed, deployed, and optimized at GPU cloud scale. You’ll gain exposure to modern ML frameworks, large-scale GPU infrastructure (including NVIDIA Hopper, Blackwell and Grace Blackwell platforms), and the collaboration between customers, engineering, and product teams.

This role offers a strong learning environment with mentorship, minimal bureaucracy, and opportunities to develop both technical and communication skills.

We are looking for an AI/ML Solutions Architect Intern to support AI-focused customers using Nebius services. This is a learning-focused role designed for students or early-career candidates who are interested in AI infrastructure, cloud platforms, and applied machine learning.

You will work closely with experienced Solutions Architects, Engineers, and Product teams to help design, document, and support AI solutions.

Your responsibilities will include:

• Assist in designing and documenting AI and ML solutions.
• Learn how distributed training and inference workloads run on multi-GPU cloud-based systems.
• Help prepare technical materials such as presentations, documentation, tutorials, or demos.
• Participate in investigation, troubleshooting and optimization tasks under guidance from senior team members.

We expect you to have:

• Currently pursuing or recently completed a degree in Computer Science, Engineering, Data Science, or a related field.
• Solid understanding of machine learning concepts and workflows.
• Familiarity with at least one ML framework such as PyTorch, TensorFlow, or JAX.
• Comfort with programming in Python (other languages are a plus).
• Interest in cloud infrastructure, GPUs, and large-scale AI systems.
• Strong communication skills and a willingness to learn and ask questions.

Nice to have:

• Exposure to Kubernetes, Docker, or other container technologies.
• Familiarity with Git or basic DevOps workflows.
• Coursework or projects involving distributed systems, MLOps, or deep learning.
• Experience with AI projects from school, hackathons, or personal work.

Tools you may learn and use:

• Programming: Python (plus exposure to Go, Java, or C++).
• ML Frameworks: PyTorch, TensorFlow, JAX, Hugging Face.
• Infrastructure & DevOps: Docker, Git, Kubernetes, Terraform.
• Schedulers & Orchestration: Kubernetes, Slurm (learning-focused).

What we offer

• Competitive salary (ranging from 8,160 – 12,240 USD) and comprehensive benefits package.
• Mentorship from experienced AI, ML, and cloud infrastructure professionals.
• Hands-on experience with real customer workloads and production systems.
• Remote work flexibility and a collaborative, supportive team environment.
• Opportunity to be considered for a full-time role after the Program.

We’re growing and expanding our products every day. If you’re up to the challenge and are excited about AI and ML as much as we are, join us!

The role 

We are building a high-performance AI inference platform for developer-native teams running latency- and cost-sensitive workloads at scale.

In AI infrastructure, PoC success does not always guarantee production success. This role exists to ensure that what we commit to with customers is scalable, efficient, and aligned with platform strategy.

We are looking for a Presales Architect to become a foundational technical partner to our customers and a force multiplier for Sales and Engineering. You will shape complex AI workloads from first discovery through production feasibility validation, ensuring technical rigor, economic viability, and scalable architecture decisions.

You will operate at the intersection of customer ambition, engineering reality, and commercial growth, influencing:

• Revenue quality
• Engineering focus
• Product evolution
• Customer trust at scale

You’re welcome to work remotely from the United States.

Your responsibilities will include: 

Strategic Technical Discovery

• Lead deep technical discovery with engineering teams and technical founders
• Understand model requirements, traffic expectations, latency constraints, GPU economics, and system dependencies
• Translate customer ambition into production-feasible architecture.
• Identify hidden technical risks early

Commercial Acceleration

• Partner tightly with Sales on strategic deals
• Influence deal strategy through architectural clarity
• Prevent misaligned commitments before engineering allocation
• Increase PoC-to-production conversion by ensuring technical realism

PoC Architecture & Validation

•  Define measurable success criteria (latency, TTFT, throughput, cost envelope)
• Classify workload complexity and required optimization depth
• Align appropriate resources (ML Solution Architects, engineering, GPU capacity, etc.)
• Drive structured Go / No-Go decisions
• Prevent uncontrolled customization or hidden R&D

Pattern Recognition & Platform Leverage

• Identify recurring configuration patterns across customers
• Quantify demand for advanced optimizations (quantization, speculative decoding, etc.)
• Surface structured insights to Product and Engineering
• Help evolve platform capabilities based on real workload data

We expect you to have: 

• Deep understanding of AI inference systems and GPU-backed infrastructure
• Experience with LLM workloads and performance-sensitive environments
• Experience with inference frameworks and libraries (e.g., vLLM, SGLang, TensorRT-LLM).
• Ability to reason about latency, throughput, cost, and architecture tradeoffs
• Strong customer presence with engineering-first organizations
• Comfort challenging assumptions and pushing back constructively
• Commercial awareness – you understand that engineering time is a strategic resource

Preferred Technical Stack

• Programming Languages– Python
• Frameworks and Libraries– vLLM, SGLang, TensorRT-LLM, OpenAI/Anthropic SDKs
• Frameworks for Agentic Pipelines : Langchain / Langsmith / smolagents / equivalent
• API and Web Frameworks– FastAPI, Flask
• MLOps and DevOps tools– Kubernetes (K8s), Docker, Git
• Cloud Platforms– AWS (SageMaker, Bedrock), GCP (Vertex AI), Azure (Azure ML)

What Success Looks Like

• Strategic deals are technically sound before engineering engagement
• PoCs are clearly scoped and economically justified
• Engineering capacity is allocated predictably
• Conversion to production improves
• Customers view you as a trusted architectural advisor

Key employee benefits in the US:

• 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, ranging from $152k - 228k base + RSU's and performance bonus's.

Customer Experience:

The Customer Experience Team at Nebius AI Cloud involves tackling customers’ challenges and directly impacting their success by solving real-world AI and ML problems at massive GPU cloud scale. You’ll not only resolve issues, but play a key role in shaping clients’ business success by optimizing their AI solutions. Working with advanced GPUs such as H200, B200 and GB200, as well as modern ML frameworks, you’ll influence the development of the Nebius AI Cloud and gain experience at the intersection of infrastructure and AI. With minimal bureaucracy, you’ll have the freedom to innovate, take ownership and drive change. Opportunities for growth are abundant in this vibrant and supportive professional community.

The role

We are seeking a highly skilled and customer-focused professional to join our team as a Solutions Architect specializing in Cloud infrastructure and MLOps. As a Cloud Solutions Architect, you will play a pivotal role in designing and implementing cutting-edge solutions for our clients, leveraging cloud technologies for ML/AI teams and becoming a trusted technical advisor for building their pipelines.

You’re welcome to work remotely from the US or Canada.

Your responsibilities will include: 

• Act as a trusted advisor to our clients, providing technical expertise and guidance throughout the engagement. Conduct PoC, workshops, presentations, and training sessions to educate clients on GPU cloud technologies and best practices.
• Collaborate with clients to understand their business requirements and develop solution architecture that align with their needs: design and document Infrastructure as code solutions, documentation and technical how-tos in collaboration with support engineers and technical writers.
• Help customers to optimize pipeline performance and scalability to ensure efficient utilization of cloud resources and services powered by Nebius AI.
• Act as a single point of expertise of customer scenarios for product, technical support, marketing teams.
• Assist to Marketing department efforts during events (Hackathons, conferences, workshops, webinars, etc.)

We expect you to have: 

• 5 - 10 + years of experience as a cloud solutions architect, system/network engineer, developer or a similar technical role with a focus on cloud computing
• Strong hands-on experience with IaC and configuration management tools (preferably Terraform/Ansible), Kubernetes, skills of writing code in Python
• Solid understanding of GPU computing practices for ML training and inference workloads, GPU software stack components, including drivers, libraries (e.g. CUDA, OpenCL)
• Excellent communication skills
• Customer-centric mindset

It will be an added bonus if you have: 

• Hands-on experience with HPC/ML orchestration frameworks (e.g. Slurm, Kubeflow)
• Hands-on experience with deep learning frameworks (e.g. TensorFlow, PyTorch)
• Solid understanding of cloud ML tools landscape from industry leaders (NVIDIA, AWS, Azure, Google) 

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, ranging from 225k - 315k OTE (On-Target Earnings) and equity based on your experience, skills, and location.

Join Nebius Today!

• Build prototypes and demos across the product portfolio — serverless inference, databases, MLflow, MLOps, and vertical use cases in Physical AI and HCLS — that become assets for sales, product, and engineering teams
• Support new customers hands-on through POC design, technical onboarding, and validation; act as the bridge between their ML team and the platform during the critical first months
• Go deep on emerging applied AI — new training techniques, inference optimizations, agentic architectures, new frameworks — and turn findings into working prototypes, writeups, and product recommendations
• Feed the product roadmap with specific, grounded feedback; be the voice of "here's what broke in three customer POCs last month and here's what needs to change"
• Develop reusable technical assets — notebooks, reference architectures, benchmark results — that reduce onboarding friction at scale

• You've fine-tuned large models, debugged distributed training jobs, built production RAG or agentic pipelines, and optimized inference on GPU infrastructure — not just read about it
• You're fluent in the modern ML stack: PyTorch, HuggingFace, CUDA fundamentals, Kubernetes for ML, MLflow or equivalent, vector databases
• You've worked with enterprise ML teams — whether as a solutions engineer, customer engineer, or an ML engineer who collaborated closely with customers
• You read papers and implement them — not for credit, but because it's how you stay sharp
• You communicate with calibration: you can explain activation checkpointing tradeoffs to an ML engineer in the morning and the cost implication to a CTO in the afternoon

• Experience in any of our vertical domains: Physical AI / robotics / simulation, HCLS (drug discovery, medical imaging, clinical NLP), or enterprise AI application development
• Familiarity with MLOps at scale (Kubeflow, Metaflow, Argo, Ray)
• Prior work at a cloud provider or AI infrastructure company
• You've shared technical work publicly — notebooks, talks, blog posts that people actually use

• Competitive salary and comprehensive benefits package.
• Opportunities for professional growth within Nebius.
• Flexible working arrangements.
• A dynamic and collaborative work environment that values initiative and innovation.

The role

We seek an experienced Senior ML Solutions Architect to support customers leveraging Nebius Token Factory's serverless inference platform for open-source LLMs across multiple modalities. In this role, you will be collaborating with clients to design and implement customized LLM-based solution and architect scalable AI applications using our served models, and working together with our backend team to improve our platform to match the clients' needs.

Your responsibilities will include: 

• Design and implement LLM-based solutions using Nebius Token Factory’s inference services to drive business value and support customer goals.
• Build production-ready applications leveraging our serverless LLM APIs, including multimodal models (text, vision, audio) and domain-specific models.
• Provide technical expertise in prompt engineering, RAG architectures, model selection, and inference optimization.
• Collaborate with product and engineering teams to surface customer feedback and shape the platform roadmap.
• Guide customers in scaling from POC to production with a focus on performance, reliability, and cost efficiency.

We expect you to have: 

• 5+ years of experience in ML/AI systems, with at least 2 years focused on LLMs and generative AI.
• Deep knowledge of the LLM ecosystem, including model architectures and fine-tuning approaches.
• Hands-on experience with:
  • Prompt engineering and LLM pipeline development, including evaluation.
  • Agentic frameworks such as Langchain, Langsmith, smolagents, or equivalent.
  • Vector databases and RAG implementation patterns.
  • Deploying LLM-powered applications using APIs from OpenAI, Anthropic, or open-source models.
• Strong Python programming skills.
• Excellent communication skills, with the ability to clearly explain technical concepts to diverse audiences.

It will be an added bonus if you have: 

• Experience with inference frameworks and libraries (e.g., vLLM, SGLang, TensorRT-LLM, Transformers).
• Familiarity with inference optimization techniques such as quantization, batching, caching, and routing.
• Work with multimodal AI models (e.g., vision-language, speech).
• Proficiency with DevOps tools (Docker, Kubernetes).
• Contributions to open-source ML/AI projects.

Preferred tooling:

• Programming Languages– Python
• ML Frameworks and Libraries– vLLM, SGLang, TensorRT-LLM, Transformers, OpenAI/Anthropic SDKs
• Frameworks for Agentic Pipelines : Langchain / Langsmith / smolagents / equivalent
• API and Web Frameworks– FastAPI, Flask
• MLOps and DevOps tools– Kubernetes (K8s), Docker, Git
• Cloud Platforms– AWS (SageMaker, Bedrock), GCP (Vertex AI), Azure (Azure ML)

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, ranging from $225k - $315k OTE (On-Target Earnings) and equity based on your experience, skills, and location.

Join Nebius Today!

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