About Sovrn

Every interesting company solves important problems for other people.  Sovrn is a Software and Data business that helps Open Web businesses be and remain independent.  We help them understand their business better, operate more efficiently, and make & keep more money.

• We believe in the freedom and free-flow of information.
• We believe the Open Web is the largest source of this information.
• We believe in helping Open Web businesses be and remain Independent.

Through Software products and Data solutions we help our customers:

• Understand their business better, so they can make better decisions
• Operate their business more efficiently, so they can invest in what matters most
• Make (and Keep) more money, so they control their own destiny

About the Role

We're looking for a Principal Software Engineer with deep roots in adtech infrastructure and a genuine conviction about what AI-native engineering looks like in practice. This role is backend-focused at its core — you'll work on the systems that power our exchange, from bid request processing and traffic shaping to data pipelines and auction logic. But we're also at an inflection point in how we build software, and we want this person to help lead us through it.

From a generative/agentic AI capabilities standpoint, we already use LLMs and agentic tooling across our data stack and, we’re looking for is someone who can help us take that from general adoption to intentional practice — who has strong opinions about where AI creates real leverage in a high-throughput adtech environment, and who can bring the rest of the engineering organization along with them.

Languages/components/tools in our stack: Java, Kafka (Redpanda), AWS, Databricks, Datadog, ONYX.

What you'll be doing:

• Own the design and evolution of backend systems that operate at exchange scale — high throughput, low latency, always on
• Lead architectural decisions across auction infrastructure, bid processing, traffic shaping, and supply-side integrations
• Establish and champion AI engineering practices across the team — from prompt engineering and RAG patterns to agentic workflow design and LLM evaluation
• Identify high-leverage opportunities to apply AI in our adtech stack: traffic scoring and filtering, bid shading, yield optimization, anomaly detection, and automated decisioning
• Set standards for how we evaluate, trust, and operate AI-powered systems in production — including observability, fallback behavior, and model governance
• Drive technical standards, design reviews, and engineering best practices across a senior team
• Partner with product, data science, and platform teams to ship end-to-end
• Help the broader engineering team build fluency and confidence with AI tooling, not just tolerance of it
• Mentor and level up engineers through code review, design collaboration, and hands-on guidance

A successful candidate will have:

• 10+ years of software engineering experience, with a strong backend track record
• 5+ years working specifically in adtech infrastructure — SSP, DSP, exchange, or ad server environments
• Deep fluency in the programmatic ecosystem: OpenRTB, bid request/response flows, auction mechanics, supply path optimization, or similar
• Experience building and operating systems at exchange scale — high QPS, strict latency budgets, fault tolerance
• Demonstrated experience leading AI or agentic engineering efforts in production environments — not just experimentation, but shipped, operated, and iterated on
• Hands-on experience with LLM integration patterns: RAG, tool use, multi-step agentic workflows, prompt engineering, and evaluation frameworks
• Strong distributed systems fundamentals: messaging, caching, service architecture, observability
• Comfort driving technical decisions in ambiguous, fast-moving environments
• A point of view on where AI is and isn't the right tool — and the credibility to make that case

Location: This position is based in our Boulder Colorado or New York City office. Applicants need to be able to commute/travel to our Boulder or New York City location. For exceptional candidates we would consider remote locations in these states: AZ, CA, CO, GA, MD, MA, MI, MO, NJ, NY, NC, OK, TX, UT, WA. #LI-Hybrid #LI-Remote

We understand that no candidate is perfectly qualified for any job. Experience comes in different forms; many skills are transferable; and passion goes a long way. Even more important than your resume is a clear demonstration of accountability and the ability to thrive in a fluid and collaborative environment. We expect you to learn new things in this role and encourage you to apply if your experience is close to what we’re looking for.

Sovrn Core Values: Candid, Customer Empathy, Learning, Scrappy, Second Order Thinking

Compensation and Benefits

The base pay range for this position is $200,000 to $240,000 annually. In addition to the base salary, the total compensation package includes bonus, equity, and benefits. Actual earnings may vary depending on the candidate’s direct experience, skills, industry knowledge, and location.

Sovrn offers a full slate of benefits from competitive salaries, stock options, medical, dental, and vision coverage, short and long-term disability, life insurance, 11 paid holidays, flexible vacation, commuter benefits, a 401(k) plan and match, and a paid parental leave program.

Job Posting

Applications will be accepted until July 1, 2026.

Equal Opportunity Employer

Sovrn is proud to be an Equal Opportunity Employer and provides equal employment opportunities to all employees and applicants regardless of race, color, religion, gender, gender identity, age, national origin, disability, parental or pregnancy status, marriage and civil partnership, sexual orientation, veteran status, or any other characteristic protected by law.

Recruitment Agencies

Sovrn does not accept agency resumes. Please do not forward resumes to our jobs alias or Sovrn employees. Sovrn is not responsible for any fees related to unsolicited resumes.

About Sovrn

Every interesting company solves important problems for other people.  Sovrn is a Software and Data business that helps Open Web businesses be and remain independent.  We help them understand their business better, operate more efficiently, and make & keep more money.

• We believe in the freedom and free-flow of information.
• We believe the Open Web is the largest source of this information.
• We believe in helping Open Web businesses be and remain Independent.

Through Software products and Data solutions we help our customers:

• Understand their business better, so they can make better decisions
• Operate their business more efficiently, so they can invest in what matters most
• Make (and Keep) more money, so they control their own destiny

About the Role

We’re looking for a Principal Software Engineer (Data) with deep roots in adtech data infrastructure and a genuine conviction about what AI-native data engineering looks like in practice. This is a specialized principal-level engineering role — one that carries all the architectural ownership and technical leadership expectations of a Principal Software Engineer, focused on Sovrn’s Data Collective.

From a generative/agentic AI capabilities standpoint, we already use LLMs and agentic tooling across our data stack and, we’re looking for is someone who can help us take that from general adoption to intentional practice — who has strong opinions about where AI creates real leverage in a high-throughput adtech environment, and who can bring the rest of the engineering organization along with them.

Languages/components/tools in our stack: Python, Pyspark, Kafka, Databricks, AWS

What you'll be doing:

Data Platform & Architecture

• Own the design and evolution of data platform systems that operate at exchange scale; high throughput, real-time streaming, and always-on batch pipelines
• Lead architectural decisions across data infrastructure: pipeline design, data modeling, lakehouse architecture, and data services layers
• Specify data platform components and configurations required for pipeline implementation; define pipeline observability to understand and improve performance at massive scale
• Research, implement, and evolve methods to process and democratize data across the organization
• Drive technical standards, design reviews, and engineering best practices across a senior team
• Partner with product, data science, and platform teams to ship end-to-end

AI & Agentic Engineering Leadership

• Establish and champion AI engineering practices across the team, from prompt engineering and RAG patterns to agentic workflow design, LLM evaluation, and progressive implementation of agentic design patterns
• Identify high-leverage opportunities to apply AI in our data stack: intelligent pipeline optimization, anomaly detection, automated data quality, forecasting, and LLM-powered data services
• Lead the evolution of our existing LLM and agentic tooling from passive use to intentional, well-architected integration within our data platform
• Set standards for how we evaluate, trust, and operate AI-powered systems in production, including observability, fallback behavior, and model governance
• Help the broader engineering team build fluency and confidence with AI tooling, not just tolerance of it

Collaboration & Mentorship

• Provide domain expertise across the organization to enable business growth through data services and data models
• Provide counsel to all consumers and stakeholders of data to enable efficient and impactful use of our data assets
• Mentor and level up engineers through code review, design collaboration, and hands-on guidance; foster a culture of innovation and continuous learning
• Operate with high autonomy across ambiguous, high-impact problems

A successful candidate will have:

• 10+ years of software engineering experience, with a strong data engineering and backend track record
• 5+ years working specifically in adtech data infrastructure, SSP, DSP, exchange, or ad server environments
• Deep fluency in the programmatic ecosystem: OpenRTB, bid request/response flows, auction mechanics, supply path optimization, or similar
• Excellent understanding of real-time streaming and batch pipelines, big data, and data lakes; hands-on experience in distributed data processing in the AWS ecosystem
• Strong understanding of second-layer big data platforms such as Snowflake and Databricks, applicable use cases, best practices, implementation, and support considerations
• Strong experience in structured, unstructured, and semi-structured data techniques; metadata management, data lineage, and data governance
• Experience with data security and compliance (PII, CCPA, GDPR, etc.)
• Demonstrated experience leading AI or agentic engineering efforts in production environments; not just experimentation, but shipped, operated, and iterated on
• Hands-on experience with LLM integration patterns: RAG, vector DBs, tool use, multi-step agentic workflows, prompt engineering, and evaluation frameworks
• Ability to clearly document and communicate architectural concepts at multiple levels; ability to lead problem definition, solution designs, and implementation work plans
• Understanding of DevOps and SRE practices
• Comfort driving technical decisions in ambiguous, fast-moving environments
• A point of view on where AI is and isn’t the right too, and the credibility to make that case

Location: This position is based in our Boulder Colorado or New York City office. Applicants need to be able to commute/travel to our Boulder or New York City location. For exceptional candidates we would consider remote locations in these states: AZ, CA, CO, GA, MD, MA, MI, MO, NJ, NY, NC, OK, TX, UT, WA. #LI-Remote, #LI-Hybrid

We understand that no candidate is perfectly qualified for any job. Experience comes in different forms; many skills are transferable; and passion goes a long way. Even more important than your resume is a clear demonstration of accountability and the ability to thrive in a fluid and collaborative environment. We expect you to learn new things in this role and encourage you to apply if your experience is close to what we’re looking for.

Sovrn Core Values: Candid, Customer Empathy, Learning, Scrappy, Second Order Thinking

Compensation and Benefits

The base pay range for this position is $200,000 to $240,000 annually. In addition to the base salary, the total compensation package includes bonus, equity, and benefits. Actual earnings may vary depending on the candidate’s direct experience, skills, industry knowledge, and location.

Sovrn offers a full slate of benefits from competitive salaries, stock options, medical, dental, and vision coverage, short and long-term disability, life insurance, 11 paid holidays, flexible vacation, commuter benefits, a 401(k) plan and match, and a paid parental leave program.

Job Posting

Applications will be accepted until July 1, 2026.

Equal Opportunity Employer

Sovrn is proud to be an Equal Opportunity Employer and provides equal employment opportunities to all employees and applicants regardless of race, color, religion, gender, gender identity, age, national origin, disability, parental or pregnancy status, marriage and civil partnership, sexual orientation, veteran status, or any other characteristic protected by law.

Recruitment Agencies

Sovrn does not accept agency resumes. Please do not forward resumes to our jobs alias or Sovrn employees. Sovrn is not responsible for any fees related to unsolicited resumes.

About the role

The next generation of behavioral health will be built on agentic AI — systems that don't just predict or summarize, but act: scheduling, coordinating care, drafting clinical documentation, surfacing risk, and orchestrating the hundreds of micro-decisions that today consume clinician and operator time.

Charlie Health's ambition is to build the most advanced agentic systems in healthcare. We are investing in a multi-agent architecture grounded in a real-time clinical signal graph, governed by HIPAA-native infrastructure, and deployed against measurable patient outcomes in a national virtual IOP.

We are hiring a Senior Principal AI Engineer to lead the design, development, and deployment of these systems across our platform. This is a hands-on technical leadership role with deep cross-functional reach into Engineering, Product, Data, Clinical, and executive leadership.

Responsibilities

• Serve as the technical owner of agentic AI at Charlie Health, from architecture and model strategy through production deployment and ongoing reliability.
• Lead the development of clinical operations agents that handle scheduling, intake, insurance verification, referral triage, and care coordination, replacing brittle human handoffs with auditable, supervised AI workflows.
• Lead the development of clinician copilots for real-time documentation, risk surfacing, and treatment-plan support, integrated with our EHR and clinical data platform.
• Define and build the agentic substrate: eval frameworks, observability, guardrails, tool registries, prompt and policy versioning, PHI-safe logging, and simulation environments.
• Own the model strategy across frontier models, fine-tunes, distilled specialists, and classic ML, and articulate it clearly to executive and clinical leadership.
• Partner with Product, Clinical, and Data teams to identify high-leverage opportunities for AI across patient and operational experiences.
• Establish best practices around AI reliability, safety, evaluation, and explainability in a regulated clinical environment.
• Mentor engineers and data scientists across the organization and help foster a strong culture of technical excellence and responsible AI innovation.

What you'll work with

• An executive team that has committed to an AI-native operating model and is funding it.
• An AI Guild of senior engineers, data scientists, and clinical operators piloting AI across the company today.
• A modern data foundation including Snowflake, dbt, pgvector, and an emerging Intelligence Layer that exposes patient and operational signals as first-class events.
• Live workstreams in clinical documentation, referral-source conversion modeling, scheduling intelligence, and clinician productivity.
• A national virtual IOP serving patients across 41 states, with the regulated data and outcome signal needed to train, evaluate, and improve real systems.

Why this role is hard

Behavioral health is one of the most demanding applied-AI environments. Patients are often in acute distress. The regulatory surface spans HIPAA, state-by-state telehealth and licensure law, and rising FDA scrutiny of clinical decision support. AI systems in this environment must be observable, reversible, and defensible to clinicians, to regulators, and to internal review.

We are looking for an engineer who finds those constraints clarifying rather than limiting.

Requirements

• 10+ years of software engineering experience, with several recent years focused on LLM- or ML-powered systems in production.
• Hands-on experience designing and operating agentic systems, including tool use, planning, multi-agent orchestration, eval design, and failure-mode analysis.
• Track record of owning a complex technical initiative end-to-end, including evaluation infrastructure, observability, on-call, and regulatory or compliance review.
• Strong communication skills and comfort operating across registers, from executive strategy conversations to engineering code review.
• Genuine interest in healthcare and in applying AI responsibly in a regulated, high-stakes domain.
• BS, MS, or PhD in Computer Science, Machine Learning, Engineering, Mathematics, or a related technical field, or equivalent practical experience.
• Able to work a hybrid schedule of 4 days/week in our NYC office and located within 75 minutes' commuting distance of the office

Benefits

Charlie Health is pleased to offer comprehensive benefits to all full-time, exempt employees. Read more about our benefits here.

Additional Information

The total target base compensation for this role will be between $350,000 and $600,000 per year at the commencement of employment. Please note, pay will be determined on an individualized basis and will be impacted by location, experience, expertise, internal pay equity, and other relevant business considerations. Further, cash compensation is only part of the total compensation package, which, depending on the position, may include stock options and other Charlie Health-sponsored benefits.  #LI-HYBRID

About the role

The next generation of behavioral health will be built on agentic AI — systems that don't just predict or summarize, but act: scheduling, coordinating care, drafting clinical documentation, surfacing risk, and orchestrating the hundreds of micro-decisions that today consume clinician and operator time.

Charlie Health's ambition is to build the most advanced agentic systems in healthcare. We are investing in a multi-agent architecture grounded in a real-time clinical signal graph, governed by HIPAA-native infrastructure, and deployed against measurable patient outcomes in a national virtual IOP.

We are hiring a Senior Principal AI Engineer to lead the design, development, and deployment of these systems across our platform. This is a hands-on technical leadership role with deep cross-functional reach into Engineering, Product, Data, Clinical, and executive leadership.

Responsibilities

• Serve as the technical owner of agentic AI at Charlie Health, from architecture and model strategy through production deployment and ongoing reliability.
• Lead the development of clinical operations agents that handle scheduling, intake, insurance verification, referral triage, and care coordination, replacing brittle human handoffs with auditable, supervised AI workflows.
• Lead the development of clinician copilots for real-time documentation, risk surfacing, and treatment-plan support, integrated with our EHR and clinical data platform.
• Define and build the agentic substrate: eval frameworks, observability, guardrails, tool registries, prompt and policy versioning, PHI-safe logging, and simulation environments.
• Own the model strategy across frontier models, fine-tunes, distilled specialists, and classic ML, and articulate it clearly to executive and clinical leadership.
• Partner with Product, Clinical, and Data teams to identify high-leverage opportunities for AI across patient and operational experiences.
• Establish best practices around AI reliability, safety, evaluation, and explainability in a regulated clinical environment.
• Mentor engineers and data scientists across the organization and help foster a strong culture of technical excellence and responsible AI innovation.

What you'll work with

• An executive team that has committed to an AI-native operating model and is funding it.
• An AI Guild of senior engineers, data scientists, and clinical operators piloting AI across the company today.
• A modern data foundation including Snowflake, dbt, pgvector, and an emerging Intelligence Layer that exposes patient and operational signals as first-class events.
• Live workstreams in clinical documentation, referral-source conversion modeling, scheduling intelligence, and clinician productivity.
• A national virtual IOP serving patients across 41 states, with the regulated data and outcome signal needed to train, evaluate, and improve real systems.

Why this role is hard

Behavioral health is one of the most demanding applied-AI environments. Patients are often in acute distress. The regulatory surface spans HIPAA, state-by-state telehealth and licensure law, and rising FDA scrutiny of clinical decision support. AI systems in this environment must be observable, reversible, and defensible to clinicians, to regulators, and to internal review.

We are looking for an engineer who finds those constraints clarifying rather than limiting.

Requirements

• 10+ years of software engineering experience, with several recent years focused on LLM- or ML-powered systems in production.
• Hands-on experience designing and operating agentic systems, including tool use, planning, multi-agent orchestration, eval design, and failure-mode analysis.
• Track record of owning a complex technical initiative end-to-end, including evaluation infrastructure, observability, on-call, and regulatory or compliance review.
• Strong communication skills and comfort operating across registers, from executive strategy conversations to engineering code review.
• Genuine interest in healthcare and in applying AI responsibly in a regulated, high-stakes domain.
• BS, MS, or PhD in Computer Science, Machine Learning, Engineering, Mathematics, or a related technical field, or equivalent practical experience.
• Able to work a hybrid schedule of 4 days/week in our NYC office and located within 75 minutes' commuting distance of the office

Benefits

Charlie Health is pleased to offer comprehensive benefits to all full-time, exempt employees. Read more about our benefits here.

Additional Information

The total target base compensation for this role will be between $350,000 and $600,000 per year at the commencement of employment. Please note, pay will be determined on an individualized basis and will be impacted by location, experience, expertise, internal pay equity, and other relevant business considerations. Further, cash compensation is only part of the total compensation package, which, depending on the position, may include stock options and other Charlie Health-sponsored benefits.  #LI-HYBRID

Who we are

About Stripe

Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career.

About the team

The Stripe Assistant team is transforming how users interact with Stripe by building an intelligent and proactive assistant that not only answers users’ queries but efficiently resolves issues and provides valuable business insights. We leverage LLMs and agentic systems to elevate the user experience across Stripe—from the dashboard to support surfaces—and we enable other teams to build and integrate their AI agents on our platform. We’re evolving from a helpful support tool to a trusted pilot that anticipates, optimizes, and executes on behalf of our users.

What you’ll do

As a Senior Machine Learning Engineer on the Stripe Assistant team, you’ll own the end-to-end ML and agent architecture that makes Stripe Assistant safe, reliable, and deeply useful. You’ll set the strategy for how the Assistant executes high-trust actions, delivers accurate analytical answers across Stripe and the broader web, orchestrates capabilities across many tools and agents, and grounds responses in authoritative Stripe and user data—so users can resolve issues quickly and confidently.

You’ll drive conversation continuity and personalization across surfaces, evolve the Assistant into a proactive partner that anticipates user needs, and deepen its presence in the dashboard to streamline critical workflows. You’ll establish rigorous evaluation and SLOs and deliver step‑change improvements in quality, latency, cost, and availability—paving the way for configurable levels of autonomy and, ultimately, a dependable operating layer over a merchant’s Stripe account.

Responsibilities

Our team operates fluidly and here are some problems you may tackle:

• Establish trustworthy, human-in-the-loop execution for high-trust “write” actions—prioritizing user control, transparency, accountability, and auditability so customers can delegate with confidence.
• Define and evolve the Assistant’s capability and governance model across hundreds of tools and agents, balancing power, permissions, and consistency at scale.
• Raise answer quality and usefulness by grounding in authoritative Stripe knowledge and live user data, building cross-surface memory and personalization, and making the Assistant proactive and present in the dashboard.
• Explore and apply optimal machine learning methods to improve Stripe Assistant’s overall performance, including but not limited to fine-tuning LLMs with RLHF, synthetic data generation, optimizing RAG pipelines via domain‑specific embedding and retriever fine‑tuning, and automatic prompt tuning, etc.
• Make quality and reliability a product: set and meet SLOs, build rigorous evaluation and benchmarking loops, and drive sustained improvements in latency, cost, and availability.
• Lead as a tech lead: mentor and grow engineers, uphold high bars for code quality, security, observability, and operational rigor, and align cross‑functionally to ship safely and fast.

Who you are

We’re looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement.

Minimum requirements

• 5+ years in AI/ML and backend engineering.
• Applied LLM experience: RAG/embeddings, tool use/function calling, agentic planning/orchestration, fine-tuning, code generation, evaluations, etc.
• Proficient in Python (Ruby is a plus); strong distributed systems fundamentals.
• Experience working closely with product management, design, other engineers, and other cross-functional partners.

Preferred qualifications

• Experience operating ML systems at global scale with stringent SLOs—balancing reliability, latency, and cost—with privacy, security, and compliance by design.
• Experience building products where AI/ML is core; as well as balancing short-term product priorities with long-term AI/ML improvements.
• Track record building ML platforms, especially those that enable multiple teams to collaborate together.
• Strong technical leadership and communication: mentoring and elevating engineers, elevating AI/ML awareness and posture within organizations, setting architectural direction, and driving alignment in ambiguity.

Join us to build a trustworthy, proactive AI operating layer for every Stripe merchant—advancing safety, reliability, and insight at global scale. If you’re ready to help take Stripe Assistant from copilot to full autopilot and shape how businesses connect with Stripe, we’d love to hear from you.

Who we are

About Stripe

Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career.

About the team

AI is moving fast and Stripe is moving fast to adopt it. But we need to make sure we do all of this safely, and for most of these tools enterprise-grade security isn't just available off the shelf. That's where the AI security team comes in - we are responsible for building and partnering to create the right security primitives to enable Stripe to move fast safely.

This is a team that will be working in the low level components of our AI stack so both infrastructure and security experience are pluses. This space is moving fast so being comfortable with ambiguity and changes in priority are a must.

This is a foundational role — you'd be the first engineer on the team.

What you’ll do

Engineers on this team will do a mixture of partnering and advising with teams building AI infrastructure and products, and building security infrastructure primitives for AI. 

One of our major focuses will be building a security tooling layer that sits in the inference path, providing detection and protection against AI specific threats like prompt injection, jailbreaks, and tool misuse.

Responsibilities

• Build security frameworks integrated with our core AI infrastructure
• Design and implement defenses against AI specific threats like prompt injection and jailbreaks
• Collaborate with other engineering teams to ensure Stripe's AI infrastructure and products are built securely
• Support other engineering teams in integrating security primitives
• Uphold our high engineering standards and improve our codebase and processes
• Design and build systems that help define the state of the art for AI security

Who you are

We’re looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement.

Minimum requirements

• 5+ years of software development experience
• Experience independently designing and developing medium to large projects
• Experience working collaboratively across multiple engineering teams
• Experience writing high quality code in a major programming language
• Ability to drive clear next steps when working in an ambiguous environment
• Ability to think creatively and reason clearly in a complex problem domain

Preferred qualifications

• Experience developing, deploying, and maintaining reliable infrastructure systems
• Experience in security engineering
• Experience building AI agents or LLM-based systems

About the Role

The Cloud Inference team scales and optimizes Claude to serve the massive audiences of developers and enterprise companies across AWS, GCP, Azure, and future cloud service providers (CSPs). We own the end-to-end product of Claude on each cloud platform, from API integration and intelligent request routing to inference execution, capacity management, and day-to-day operations.

Within Cloud Inference, the model & inference launch team owns the validation pipeline for our inference server and load balancer on these platforms. We're responsible for every inference change — model launches, performance improvements, safeguard integrations — landing on cloud platforms with correctness, performance, and reliability intact.

This is high-leverage infrastructure work: validation has to be fast and cheap enough to run on the same accelerators that serve customers, trustworthy enough to replace manual checks, and consistent enough that a change working on Anthropic first-party means it works everywhere. This directly determines how fast frontier models and features ship to every cloud platform, and how quickly performance wins reach production — reclaiming capacity at a time when compute is our scarcest resource.

What You'll Do

• Be on the critical path for frontier model launches, bringing up inference for new model architectures and shipping them to cloud platforms in lockstep with our first-party platform
• Work with the core inference team to bring new inference features (e.g. structured sampling, prompt caching, and more) to cloud platforms, owning the platform-specific integration that gets them to production
• Identify and dive deep on the gaps that make inference behave differently across first-party and CSPs — config drift, observability, deployment patterns, hard cross-platform bugs — and fix them at the source rather than building platform-specific workarounds
• Design, build, and own the CI/CD infrastructure for the inference server and load balancer across cloud platforms, with shadow traffic, performance baselines (throughput and latency), and correctness checks that catch regressions before production
• Drive down merge-to-production cycle time by making validation faster, more parallel, and cost-effective enough to run on the same constrained accelerator pool that serves customers, without trading away reliability 
• Analyze observability data across providers to identify performance bottlenecks, cost anomalies, and regressions, and drive remediation based on real-world production workloads

You May Be a Good Fit If You:

• Have a strong interest in LLM serving; prior inference or ML experience is not required 
• Have significant software engineering experience, with a strong background in high-performance, large-scale distributed systems serving millions of users
• Have a track record of building automation or test infrastructure that measurably improved release velocity or reliability
• Have experience building or operating services on at least one major cloud platform (AWS, GCP, or Azure), with exposure to Kubernetes, Infrastructure as Code, or container orchestration
• Thrive in cross-functional collaboration with both internal teams and external partners
• Are a fast learner who can quickly ramp up on new technologies, hardware platforms, and provider ecosystems
• Are highly autonomous and take ownership of problems end-to-end, including work that falls outside your job description

Strong Candidates May Also Have Experience With:

• LLM inference optimization, batching, and caching strategies
• Capacity-constrained scheduling or shared-resource test infrastructure
• Solid understanding of multi-region deployments, request routing, load balancing, global traffic management
• Working with CSP partner teams to scale infrastructure across multiple platforms, navigating differences in networking, security, privacy, and managed service
• Proficiency in Python or Rust

About Us:

YipitData is the leading market research and analytics firm for the disruptive economy and recently raised up to $475M from The Carlyle Group at a valuation over $1B.

We analyze billions of alternative data points every day to provide accurate, detailed insights on ridesharing, e-commerce marketplaces, payments and more. Our on-demand insights team uses proprietary technology to identify, license, clean and analyze the data many of the world’s largest investment funds and corporations depend on.

For three years and counting, we have been recognized as one of Inc’s Best Workplaces. We are a fast-growing technology company backed by The Carlyle Group and Norwest Venture Partners. Our offices are located in NYC, Austin, Miami, Los Angeles (CA), Cupertino (CA), Hong Kong, Shanghai, Beijing, Guangzhou, and Singapore. We cultivate a people-centric culture focused on mastery, ownership, and transparency. About the Role:

We are seeking an AI Security Engineer to lead the implementation, monitoring, and continuous improvement of security, governance, and trust controls for AI systems across the organization. This role will focus on operationalizing AI system security controls using the Agentic Trust Framework mapped to OWASP guidance and the NIST AI RMF, with particular emphasis on observability engineering, behavioral monitoring, policy enforcement, misuse detection, and risk-informed response.

This person will serve as a bridge between Security, Engineering, Data, Platform, Compliance, and AI product teams to ensure AI systems are not only functional and performant, but also trustworthy, auditable, resilient, and aligned with enterprise governance requirements.

The ideal candidate combines technical depth in AI/ML systems, strong security and monitoring instincts, and the ability to define practical controls for complex, fast-evolving agentic and generative AI environments.

We expect U.S. based working hours with the majority of the team working East and Central Time Zones.

In this role, you will:

• Own AI behavior monitoring: Define what trustworthy and untrustworthy AI behavior looks like, and ensure it is measurable in production.
• Own AI observability standards: Establish telemetry, tracing, logging, and alerting requirements for AI systems and agentic workflows.
• Own control validation for agentic systems: Verify that guardrails, policy checks, access boundaries, and execution constraints are functioning as intended.
• Own AI security event analysis: Detect, investigate, and document suspicious, unsafe, or non-compliant AI behaviors and coordinate response.
• Own implementation support for governance frameworks: Translate governance principles into technical and operational requirements that product and platform teams can adopt.
• Own AI trust metrics and reporting: Define KPIs, KRIs, and dashboards that show leadership whether AI systems are operating within approved trust and security boundaries.
• Own continuous improvement of AI controls: Use incidents, testing, behavioral findings, and stakeholder feedback to strengthen control design and reduce residual risk over time.

You Are Likely To Succeed If:

• 5+ years of experience in one or more of the following: security engineering, detection engineering, observability engineering, site reliability engineering, application security, ML platform engineering, or AI governance implementation.
• Experience designing monitoring, logging, telemetry, or detection strategies for distributed systems, cloud services, or data-intensive applications.
• Familiarity with AI/ML system architecture, including large language models, retrieval-augmented generation, inference pipelines, model APIs, and agentic workflows.
• Experience translating governance, risk, or policy requirements into operational controls and measurable technical requirements.
• Strong understanding of security concepts such as identity and access management, least privilege, data protection, abuse prevention, auditability, and incident response.
• Experience investigating system behavior, identifying anomalies, and working cross-functionally to drive remediation.
• Hold industry certifications (or equivalent experience): CISSP, CCSP, GIAC Machine Learning Engineer (GMLE) 
• Strong written communication skills, including ability to write standards, control definitions, runbooks, and leadership-facing summaries.

Preferred Qualifications:

• Experience with AI observability tooling, tracing frameworks, or telemetry pipelines for LLM or agent-based systems.
• Experience implementing controls for AI safety, AI red teaming, prompt security, model misuse detection, or secure tool execution.
• Familiarity with Microsoft security, compliance, and AI governance ecosystems.
• Familiarity with trust and safety concepts for generative AI and autonomous systems.
• Experience supporting internal governance, risk, privacy, or compliance review processes for AI-enabled products.
• Experience building dashboards, alerts, and behavioral analytics for security or operational monitoring.
• Experience working in highly regulated or audit-sensitive environments.

What We Offer:

• Our compensation package includes comprehensive benefits, perks, and a competitive salary: 

  • We care about your personal life, and we mean it. We offer flexible work hours, flexible vacation, a generous 401K match, parental leave, team events, wellness budget, learning reimbursement, and more!
  • Your growth at YipitData is determined by the impact that you are making, not by tenure, unnecessary facetime, or office politics. Everyone at YipitData is empowered to learn, self-improve, and master their skills in an environment focused on ownership, respect, and trust. See more on our high-impact, high-opportunity work environment above!
  • The annual on-target earnings for this position is anticipated to be up to $230 ~ $280K. The final offer may be determined by a number of factors, including, but not limited to, the applicant's experience, knowledge, skills, abilities, as well as internal team benchmarks. 
  The compensation package also includes equity.

This role may be performed fully remotely within the United States. Please note that our US headquarters are located in NYC. We also have have US offices in Austin, Miami, Los Angeles (CA), and Cupertino (CA). If the remote work is performed outside of these offices, income may be subject to New York State tax withholding.

Please note that for this position, we are not able to consider candidates who currently or in the future will require visa sponsorship.

We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, marital status, disability, gender, gender identity or expression, or veteran status. We are proud to be an equal-opportunity employer.

Job Applicant Privacy Notice 

About Us:

YipitData is the leading market research and analytics firm for the disruptive economy and recently raised up to $475M from The Carlyle Group at a valuation over $1B.

We analyze billions of alternative data points every day to provide accurate, detailed insights on ridesharing, e-commerce marketplaces, payments and more. Our on-demand insights team uses proprietary technology to identify, license, clean and analyze the data many of the world’s largest investment funds and corporations depend on.

For three years and counting, we have been recognized as one of Inc’s Best Workplaces. We are a fast-growing technology company backed by The Carlyle Group and Norwest Venture Partners. Our offices are located in NYC, Austin, Miami, Los Angeles (CA), Cupertino (CA), Hong Kong, Shanghai, Beijing, Guangzhou, and Singapore. We cultivate a people-centric culture focused on mastery, ownership, and transparency. About the Role:

We are seeking an AI Security Engineer to lead the implementation, monitoring, and continuous improvement of security, governance, and trust controls for AI systems across the organization. This role will focus on operationalizing AI system security controls using the Agentic Trust Framework mapped to OWASP guidance and the NIST AI RMF, with particular emphasis on observability engineering, behavioral monitoring, policy enforcement, misuse detection, and risk-informed response.

This person will serve as a bridge between Security, Engineering, Data, Platform, Compliance, and AI product teams to ensure AI systems are not only functional and performant, but also trustworthy, auditable, resilient, and aligned with enterprise governance requirements.

The ideal candidate combines technical depth in AI/ML systems, strong security and monitoring instincts, and the ability to define practical controls for complex, fast-evolving agentic and generative AI environments.

We expect U.S. based working hours with the majority of the team working East and Central Time Zones.

In this role, you will:

• Own AI behavior monitoring: Define what trustworthy and untrustworthy AI behavior looks like, and ensure it is measurable in production.
• Own AI observability standards: Establish telemetry, tracing, logging, and alerting requirements for AI systems and agentic workflows.
• Own control validation for agentic systems: Verify that guardrails, policy checks, access boundaries, and execution constraints are functioning as intended.
• Own AI security event analysis: Detect, investigate, and document suspicious, unsafe, or non-compliant AI behaviors and coordinate response.
• Own implementation support for governance frameworks: Translate governance principles into technical and operational requirements that product and platform teams can adopt.
• Own AI trust metrics and reporting: Define KPIs, KRIs, and dashboards that show leadership whether AI systems are operating within approved trust and security boundaries.
• Own continuous improvement of AI controls: Use incidents, testing, behavioral findings, and stakeholder feedback to strengthen control design and reduce residual risk over time.

You Are Likely To Succeed If:

• 5+ years of experience in one or more of the following: security engineering, detection engineering, observability engineering, site reliability engineering, application security, ML platform engineering, or AI governance implementation.
• Experience designing monitoring, logging, telemetry, or detection strategies for distributed systems, cloud services, or data-intensive applications.
• Familiarity with AI/ML system architecture, including large language models, retrieval-augmented generation, inference pipelines, model APIs, and agentic workflows.
• Experience translating governance, risk, or policy requirements into operational controls and measurable technical requirements.
• Strong understanding of security concepts such as identity and access management, least privilege, data protection, abuse prevention, auditability, and incident response.
• Experience investigating system behavior, identifying anomalies, and working cross-functionally to drive remediation.
• Hold industry certifications (or equivalent experience): CISSP, CCSP, GIAC Machine Learning Engineer (GMLE) 
• Strong written communication skills, including ability to write standards, control definitions, runbooks, and leadership-facing summaries.

Preferred Qualifications:

• Experience with AI observability tooling, tracing frameworks, or telemetry pipelines for LLM or agent-based systems.
• Experience implementing controls for AI safety, AI red teaming, prompt security, model misuse detection, or secure tool execution.
• Familiarity with Microsoft security, compliance, and AI governance ecosystems.
• Familiarity with trust and safety concepts for generative AI and autonomous systems.
• Experience supporting internal governance, risk, privacy, or compliance review processes for AI-enabled products.
• Experience building dashboards, alerts, and behavioral analytics for security or operational monitoring.
• Experience working in highly regulated or audit-sensitive environments.

What We Offer:

• Our compensation package includes comprehensive benefits, perks, and a competitive salary: 

  • We care about your personal life, and we mean it. We offer flexible work hours, flexible vacation, a generous 401K match, parental leave, team events, wellness budget, learning reimbursement, and more!
  • Your growth at YipitData is determined by the impact that you are making, not by tenure, unnecessary facetime, or office politics. Everyone at YipitData is empowered to learn, self-improve, and master their skills in an environment focused on ownership, respect, and trust. See more on our high-impact, high-opportunity work environment above!
  • The annual on-target earnings for this position is anticipated to be up to $230 ~ $280K. The final offer may be determined by a number of factors, including, but not limited to, the applicant's experience, knowledge, skills, abilities, as well as internal team benchmarks. 
  The compensation package also includes equity.

This role may be performed fully remotely within the United States. Please note that our US headquarters are located in NYC. We also have have US offices in Austin, Miami, Los Angeles (CA), and Cupertino (CA). If the remote work is performed outside of these offices, income may be subject to New York State tax withholding.

Please note that for this position, we are not able to consider candidates who currently or in the future will require visa sponsorship.

We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, marital status, disability, gender, gender identity or expression, or veteran status. We are proud to be an equal-opportunity employer.

Job Applicant Privacy Notice 

Join Airtable as a Product Security Engineer and play a pivotal role in shaping the security of our rapidly evolving platform as we expand our AI and LLM-powered offerings. You will join the team responsible for safeguarding the application layer of Airtable’s platform.

You will partner closely with product engineering teams to build paved roads, frameworks, and automated controls that make the secure path the easy path for our engineering teams. You will help influence application security at scale, ensuring our products are secure by design.

What you'll do

• Develop self-service security frameworks and "paved roads" that allow engineering teams to ship secure code by default.
• Focus on automated guardrails for common vulnerabilities, while prioritizing deep-dive design reviews into complex business logic and data isolation issues (for example, multi-tenant isolation and authorization/permission bypasses) that automated tools cannot catch.
• Partner with product and engineering teams to review designs early, contribute to threat modeling for new features and complex initiatives, and provide clear, actionable security guidance.
• Research emerging threats and evolving best practices, specifically regarding AI and LLM safety, and implement controls to secure these workflows.
• Manage and evolve our approach to external penetration testing and bug bounties, driving remediation for findings and treating vulnerability management as an engineering problem.
• Contribute to the long-term roadmaps, metrics, and strategic planning for the security team.
• (Senior/Staff L5+) Lead complex threat modeling sessions for major product launches and define secure coding standards, and actively mentor other engineers to raise the technical security bar across the organization.

Who you are

• 4+ years of experience in product security or application security, with experience shipping production code. Please note this is not an early career position.
• You have a strong background in computer science or a related field, with proficiency in writing clean, maintainable code.
• You have deep familiarity with JavaScript or TypeScript, Node.js, and modern web application frameworks, and can reason about the security implications of systems built on them.
• You have hands-on experience securing LLM integrations and identifying prompt injection or data leakage risks.
• You are proficient in writing and reviewing code and treat security as an engineering problem to be solved with software, not just policies.
• You excel at communicating complex security risks to non-security stakeholders and enjoy collaborating cross-functionally to find solutions that balance security with engineering velocity.
• You are comfortable working in a fast-paced environment, navigating ambiguity, continuously learning about emerging threats and technologies, and contributing to long-term security strategy.

Airtable is an equal opportunity employer. We embrace diversity and strive to create a workplace where everyone has an equal opportunity to thrive. We welcome people of different backgrounds, experiences, abilities, and perspectives. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status or any characteristic protected by applicable federal and state laws, regulations and ordinances. Learn more about your EEO rights as an applicant. 

VEVRAA-Federal Contractor

If you have a medical condition, disability, or religious belief/practice which inhibits your ability to participate in any part of the application or interview process, please complete our Accommodations Request Form and let us know how we may assist you. Airtable is committed to participating in the interactive process and providing reasonable accommodations to qualified applicants.

#LI-remote

About EG

Engineers Gate (EG) is a leading investment manager founded in 2014 as a quantitative, computer-driven trading firm. Today, EG operates as a diversified, multi-strategy investment platform that combines systematic research with selective discretionary approaches. 
 
EG's multi-manager platform allows independent investment teams to pursue distinct strategies while benefiting from shared infrastructure, risk management, and operational support. The firm’s collaborative groups of researchers, engineers, and investment professionals deploy sophisticated statistical models, proprietary technology, and a centralized data platform to isolate and solve challenging problem sets in the global financial markets. 

About The Role 

We are seeking an End User Services Engineer to join our Information Technology team. In this role, you will be part of a global team responsible for ensuring reliable, secure, and high-performance technology operations across all business units. This role requires hands-on end-user support, proactive issue resolution, and ownership of IT systems that enable productivity at scale. You will support traders, executives, and research staff in a fast-paced, mission-critical environment.  

The ideal candidate will have combined deep technical knowledge with outstanding communication and problem-solving skills, consistently delivering “white glove” service while improving systems and processes. This role is ideal for an engineer who thrives on solving complex problems, values precision and responsiveness, and wants to make a direct impact on mission-critical operations within a high-performance environment. 

This is an on-site role based out of our New York City office, five days a week.

Key Responsibilities 

• Deliver high-quality technical support to users across Windows, macOS, and mobile platforms (iOS). 
• Provide prompt troubleshooting and resolution of hardware, software, network, and market data issues. 
• Manage user onboarding/offboarding, permissions, and account administration. 
• Support audio/visual systems and conferencing technologies (Zoom, Teams, Cisco). 
• Assist with mobile device configuration and management through Intune or similar platforms. 
• Support virtual desktop infrastructure (VDI) environments to enable secure and flexible remote access. 
• Utilize an IT Service Management (ITSM) platform to log, track, and resolve support requests. 
• Document solutions and maintain IT knowledge bases for recurring issues and procedures. 
• Identify opportunities for automation and workflow improvement to enhance efficiency. 
• Provide after-hours and weekend on-call coverage as part of a support rotation. 

Qualifications 

• 2+ years of IT support experience in the financial services industry. 
• Bachelor's degree in computer science, Information Technology, or related field. 
• Strong troubleshooting, communication, and prioritization skills. 
• Experience supporting enterprise desktop environments, including Windows 11, and Office 365. 
• Knowledge of identity and access management systems, user management, and security group administration. 
• Experience with endpoint management, software deployment concepts, and packaging. 
• Understanding of basic networking concepts (LAN/WAN, VPN, DNS, DHCP). 
• Familiarity with cloud-based collaboration platforms (Microsoft 365, SharePoint, OneDrive). 
• Working knowledge of AV setups and remote conferencing technologies. (e.g. Zoom) 
• Ability to work independently, manage priorities, and maintain attention to detail in a fast-paced environment. 
• Comfortable interfacing directly with traders, executives, and analysts to provide professional, high-touch support. 

What We Value 

• Clear communication: You build trust through empathy, clarity, and accountability. 
• Proactive mindset: You anticipate issues and improve systems before problems arise. 
• Ownership: You take initiative and follow through until issue is resolved. 
• Growth orientation: You stay curious, learn continuously, and adapt quickly to new tools and technologies. 

The base salary for this role is anticipated to be $100,000–$130,000, excluding potential bonuses, additional compensation, and benefits. Actual compensation will depend on various factors including skills, experience, and qualifications. 

Come Work With Us:

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

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

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

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

Position Summary:

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

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

Essential Functions & Responsibilities 

Production Architecture & AIOps

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

Cross-Functional Collaboration

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

Scaling & Performance

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

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

Qualifications & Skills:

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

Preferred Qualifications & Skills

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

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

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

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

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

Scale AI is the data foundation for AI, helping organizations build and deploy reliable production AI applications. We partner with leading enterprises and government organizations to accelerate their AI initiatives through our data annotation platform, generative AI solutions, and enterprise AI capabilities.

About the General Agents Team

The General Agents team, part of Scale’s Enterprise organization, builds robust general agents for customer use cases and applications. The team sits at the intersection of frontier agent development and real-world deployment, translating state-of-the-art reasoning and agentic capabilities into reliable, production-grade systems that drive real economic value. Our agents are scalable systems built around recurring enterprise problem domains, with a strong emphasis on generalization, extensibility, and deployment across many customers.

About the Role

As a Senior/Staff Machine Learning Engineer (MLE) on the General Agents team, you’ll play a critical role in designing, building, and deploying production-ready AI agents that solve high-impact enterprise problems. You will work across the full agent lifecycle—from model and system design to evaluation, deployment, and iteration—bridging cutting-edge agentic techniques with the constraints and requirements of real customer environments.

You will:

• Design and implement end-to-end agent systems that combine LLM reasoning, tool use, memory, and control logic to solve recurring enterprise use cases.
• Build scalable, reliable agent architectures that can be deployed across many customers with varying data, tools, and constraints.
• Develop evaluation frameworks, datasets, environments, and metrics to measure agent performance, reliability, and business impact in production settings.
• Collaborate closely with product managers, customers, data annotators, and other engineering teams to translate enterprise requirements into robust agent designs.
• Productionize frontier agent techniques (e.g., planning, multi-step reasoning and tool-use, multi-agent patterns) into maintainable, observable systems.
• Own deployment, monitoring, and iteration of agent systems, including failure analysis and continuous improvement based on real-world usage.
• Contribute to technical direction and architectural decisions for general agent development best practices and methods, with increasing scope and leadership at the Staff level.

Ideally you’d have:

• 5+ years of experience building and deploying machine learning or AI systems for real-world, production use cases.
• Strong engineering fundamentals, supported by a Bachelor’s and/or Master’s degree in Computer Science, Machine Learning, AI, or equivalent practical experience.
• Deep understanding of modern LLMs, prompt-, context-, and system-level optimization, and agentic system design.
• Proven proficiency in Python, including writing production-quality, testable, and maintainable code.
• Experience building systems that integrate models with external tools, APIs, databases, and services.
• Ability to operate in ambiguous problem spaces, balancing research-driven approaches with pragmatic product constraints.
• Strong communication skills and comfort working in customer-facing or cross-functional environments.

Nice-to-haves:

• Hands-on experience building AI agents using modern generative AI stacks (OpenAI APIs, commercial or open-source LLMs).
• Experience with agent frameworks, orchestration layers, or workflow systems (e.g., tool calling, planners, multi-agent setups).
• Familiarity with evaluation, monitoring, and observability for LLM-powered systems in production.
• Experience deploying ML systems in cloud environments and operating them at scale.
• Experience fine-tuning or adapting foundation models using methods like supervised fine-tuning (SFT), reinforcement learning with verifiable rewards (RLVR), and low-rank adaptation (LoRA) to improve agent performance on domain-specific tasks.
• Interest in shaping the future of general-purpose enterprise agents and their real-world impact.

The Public Sector software engineers (SWEs) create the core product building blocks forward-deployed teams use to develop agentic capabilities that function across multiple domains. SWEs responsibilities include building the systems required to ingest and process federal datasets to support real-time decision-making in contested environments. We develop novel agentic enabling capabilities that includes:

• Create multi-layered guardrails around agents
• Optimize data retrieval for agents
• Orchestrate fleets of asynchronous agents
• Automatically alerts users to deviations in data
• Illustrating how an agent reached a decision

As a Senior Software Engineer, you will lead the development of a vertical feature or a horizontal capability to include defining requirements with stakeholders and implementation until it is accepted by the stakeholders.

You will: Lead the design and implementation of scalable backend systems and distributed architectures for Federal customers.

• Manage the full lifecycle of feature development from requirement definition to deployment on classified networks.
• Direct the orchestration of asynchronous agent fleets to meet mission requirements.
• Lead customer engagements to translate mission needs into technical requirements.
• Own the communication with stakeholders to ensure implementation meets defined acceptance criteria.
• Conduct technical reviews and identify risks within machine learning infrastructure and model serving.
• Drive the platform roadmap by providing technical specifications for Federal product offerings.

Ideally you will have: 

• Full Stack Development: Proficiency in front-end, back-end development and infrastructure, including experience with modern web development frameworks, programming languages, and databases
• Cloud-Native Technologies: Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and experience in developing and deploying applications in a cloud-native environment. Understanding of containerization (e.g., Docker) and container orchestration (e.g., Kubernetes) is a plus
• Data Engineering: Knowledge of ETL (Extract, Transform, Load) processes and experience in building data pipelines to integrate and process diverse data sources. Understanding of data modeling, data warehousing, and data governance principles
• AI Application Integration: Familiarity with integrating Large Language Models (LLMs) and building agentic workflows. Understanding of prompt engineering, retrieval-augmented generation (RAG), and agent orchestration is beneficial.
• Problem Solving: Strong analytical and problem-solving skills to understand complex challenges and devise effective solutions. Ability to think critically, identify root causes, and propose innovative approaches to overcome technical obstacles
• Collaboration and Communication: Excellent interpersonal and communication skills to effectively collaborate with cross-functional teams, stakeholders, and customers. Ability to clearly articulate technical concepts to non-technical audiences and foster a collaborative work environment
• Adaptability and Learning Agility: Willingness to embrace new technologies, learn new skills, and adapt to defining and evolving project requirements. Ability to quickly grasp and apply new concepts and stay up-to-date with emerging trends in software engineering

As a Software Engineer on the ML Infrastructure team, you will design and build platforms for scalable, reliable, and efficient serving of LLMs. Our platform powers cutting-edge research and production systems, supporting both internal and external use cases across various environments.

The ideal candidate combines strong ML fundamentals with deep expertise in backend system design. You’ll work in a highly collaborative environment, bridging research and engineering to deliver seamless experiences to our customers and accelerate innovation across the company.

You will:

• Build and maintain fault-tolerant, high-performance systems for serving LLMs workloads at scale.
• Build an internal platform to empower LLM capability discovery.
• Collaborate with researchers and engineers to integrate and optimize models for production and research use cases.
• Conduct architecture and design reviews to uphold best practices in system design and scalability.
• Develop monitoring and observability solutions to ensure system health and performance.
• Lead projects end-to-end, from requirements gathering to implementation, in a cross-functional environment. 

Ideally you'd have:

• 5+ years of experience building large-scale, high-performance backend systems.
• Strong programming skills in one or more languages (e.g., Python, Go, Rust, C++).
• Experience with LLM serving and routing fundamentals (e.g. rate limiting, token streaming, load balancing, budgets, etc.)
• Experience with LLM capabilities and concepts such as reasoning, tool calling, prompt templates, etc.
• Experience with containers and orchestration tools (e.g., Docker, Kubernetes).
• Familiarity with cloud infrastructure (AWS, GCP) and infrastructure as code (e.g., Terraform).
• Proven ability to solve complex problems and work independently in fast-moving environments.

Nice to haves:

• Experience with modern LLM serving frameworks such as vLLM, SGLang, TensorRT-LLM, or text-generation-inference.

The Public Sector software engineers (SWEs) create the core product building blocks forward-deployed teams use to develop agentic capabilities that function across multiple domains. SWEs responsibilities include building the systems required to ingest and process federal datasets to support real-time decision-making in contested environments. We develop novel agentic enabling capabilities that includes:

• Create multi-layered guardrails around agents
• Optimize data retrieval for agents
• Orchestrate fleets of asynchronous agents
• Automatically alerts users to deviations in data
• Illustrating how an agent reached a decision

As a Staff Software Engineer, you will orchestrate the implementation of vertical features and horizontal capabilities to include mentoring other engineers on defining requirements with stakeholders and communication tradeoffs of technical implementations on feature and capabilities until they are accepted by the stakeholders.

You will: Orchestrate feature implementation across the Federal engineering team to ensure architectural consistency.

• Define technical strategy for agentic guardrails, explainability, and fleet orchestration.
• Ensure system reliability and performance across multiple security classifications and network types.
• Mentor engineers in the process of defining requirements with stakeholders and gathering acceptance.
• Communicate high-level technical trade-offs and implementation strategies to senior government stakeholders and Scale C-Suite members.
• Influence the long-term product strategy and technical roadmap for the Federal business unit.
• Consult on the architecture of AI-powered solutions for large-scale federal contracts.

Ideally you will have: 

• Full Stack Development: Proficiency in front-end, back-end development and infrastructure, including experience with modern web development frameworks, programming languages, and databases
• Cloud-Native Technologies: Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and experience in developing and deploying applications in a cloud-native environment. Understanding of containerization (e.g., Docker) and container orchestration (e.g., Kubernetes) is a plus
• Data Engineering: Knowledge of ETL (Extract, Transform, Load) processes and experience in building data pipelines to integrate and process diverse data sources. Understanding of data modeling, data warehousing, and data governance principles
• AI Application Integration: Familiarity with integrating Large Language Models (LLMs) and building agentic workflows. Understanding of prompt engineering, retrieval-augmented generation (RAG), and agent orchestration is beneficial.
• Problem Solving: Strong analytical and problem-solving skills to understand complex challenges and devise effective solutions. Ability to think critically, identify root causes, and propose innovative approaches to overcome technical obstacles
• Collaboration and Communication: Excellent interpersonal and communication skills to effectively collaborate with cross-functional teams, stakeholders, and customers. Ability to clearly articulate technical concepts to non-technical audiences and foster a collaborative work environment
• Adaptability and Learning Agility: Willingness to embrace new technologies, learn new skills, and adapt to defining and evolving project requirements. Ability to quickly grasp and apply new concepts and stay up-to-date with emerging trends in software engineering

About Scale AI

Scale AI is the data foundation for AI, helping organizations build and deploy reliable production AI applications. We partner with leading enterprises and government organizations to accelerate their AI initiatives through our data annotation platform, generative AI solutions, and enterprise AI capabilities.

Role Overview

As a Forward Deployed AI Engineer on our Enterprise team, you'll be the technical bridge between Scale AI's cutting-edge AI capabilities and our most strategic customers. You'll work with enterprise clients to understand their unique challenges, architect custom AI solutions, and ensure successful deployment and adoption of AI systems in production environments.

This is a hands-on technical role that combines deep engineering expertise with customer-facing problem solving. You'll work directly with customer engineering teams to integrate AI into their critical workflows.

Key Responsibilities

Customer Integration & Deployment

• Partner directly with enterprise customers to understand their technical infrastructure, data pipelines, and business requirements
• Design and implement custom integrations between Scale AI's platform and customer data environments (cloud platforms, data warehouses, internal APIs)
• Build robust data connectors and ETL pipelines to ingest, process, and prepare customer data for AI workflows
• Deploy and configure AI models and agents within customer security and compliance boundaries

AI Agent Development

• Develop production-grade AI agents tailored to customer use cases across domains like customer support, data analysis, content generation, and workflow automation
• Architect multi-agent systems that orchestrate between different models, tools, and data sources
• Implement evaluation frameworks to measure agent performance and iterate toward business objectives
• Design human-in-the-loop workflows and feedback mechanisms for continuous agent improvement

Prompt Engineering & Optimization

• Create sophisticated prompt engineering strategies optimized for customer-specific domains and data
• Build and maintain prompt libraries, templates, and best practices for customer use cases
• Conduct systematic prompt experimentation and A/B testing to improve model outputs
• Implement RAG (Retrieval Augmented Generation) systems and fine-tuning pipelines where appropriate

Technical Leadership & Collaboration

• Serve as the primary technical point of contact for strategic enterprise accounts
• Collaborate with customer data scientists, ML engineers, and software developers to ensure smooth integration
• Provide technical training and knowledge transfer to customer teams
• Work closely with Scale's product and engineering teams to translate customer needs into product improvements
• Document technical architectures, integration patterns, and best practices

Problem Solving & Innovation

• Debug complex technical issues across the entire stack, from data pipelines to model outputs
• Rapidly prototype solutions to unblock customers and prove out new use cases
• Stay current on the latest AI/ML research and tools, bringing innovative approaches to customer problems
• Identify opportunities for productization based on common customer patterns

Required Qualifications

• 4+ years of software engineering experience with strong fundamentals in data structures, algorithms, and system design
• Production Python expertise with experience in modern ML/AI frameworks (e.g., LangChain, LlamaIndex, HuggingFace, OpenAI API)
• Experience with cloud platforms (AWS, GCP, or Azure) and modern data infrastructure
• Strong problem-solving skills with the ability to navigate ambiguous requirements and rapidly iterate toward solutions
• Excellent communication skills with the ability to explain complex technical concepts to both technical and non-technical audiences

Preferred Qualifications

Agent Development Wiz

• Deep understanding of LLMs including prompting techniques, embeddings, and RAG architectures
• Experience building and deploying AI agents or autonomous systems in production
• Knowledge of vector databases and semantic search systems
• Contributions to open-source AI/ML projects

Infrastructure Guru

• Experience with containerization (Docker, Kubernetes) and CI/CD pipelines
• Experience using Terraform, Bicep, or other Infrastructure as Code (IaC) tools
• Previous work in a devops, platform, or infra role
• Familiarity with enterprise security, compliance, and governance requirements (SOC 2, GDPR, HIPAA)

Customer Product Whisperer

• Proven ability to work with customers in a technical consulting, solutions engineering, or product engineering role
• Domain expertise in verticals like finance, healthcare, government, or manufacturing
• Experience with technical enablement or teaching programs

Sample Projects

The following are some examples of the types of projects we’ve worked on with customers. All of these projects leverage customer data, integrate directly into customers’ existing systems, and are deployed on their infrastructure.

Deep Research for Due Diligence

For a global professional services firm, we developed a sophisticated deep research agent to assist in due diligence. This agent employs a multi-agent architecture for robust fact-checking, integrates several internal MCP tools, and processes complex, unstructured data sources. This solution reliably saves employees hundreds of hours weekly.

Churn Prediction

Working with a TelCo organization, we built a model utilizing customer data to predict churn likelihood. The system then curates personalized offers based on this prediction. This model was integrated into a "next best action" copilot, enabling call center agents to proactively surface relevant offers to customers, leading to a significant reduction in churn.

Data Extraction Voice Agent

We partnered with a healthcare organization to create a lifelike voice agent and avatar designed to gather unstructured health information from patients. Engineered for low latency, the agent adeptly manages conversational flow, adheres to safety guardrails, and efficiently handles data extraction. This automation saves the organization's nurses hundreds of hours each week.

Visa sponsorship is not offered for this role.
Learn More about Value Engineering at Celonis Here

Role Description

• As a Senior Value Engineering Consultant specializing in the Energy Sector, you are pushing the envelope in solving business-critical problems for our strategic customers across utilities, oil & gas, and renewable energy. You will be working with our most strategic energy clients, understanding their unique objectives—from grid modernization and asset lifecycle management to navigating the energy transition—and building Celonis solutions using the world’s leading Process Intelligence (PI) platform in combination with the largest AI and ML technology partners, such as Microsoft, OpenAI, and Databricks.
  
  With Celonis’ Process Intelligence (PI) platform, we feed operational context to AI so it understands our customers’ complex business realities (bridging IT and OT systems) and enables them to industrialize AI, unlocking real ROI on deployments at scale. You will prototype these solutions, demonstrate their value to Energy Executives, and ensure successful implementation, adoption, and value realization in order to increase the footprint of Celonis across the global energy landscape.

Key Responsibilities:

• AI Discovery & Solutioning: Understand energy customers' AI strategy and sector-specific challenges (e.g., predictive maintenance, outage management, supply chain resilience, or regulatory compliance). As a Celonis product and energy domain expert, find the best problem-solution fit and translate customer requirements into innovative solutions that move the needle.

• Pre- and Post-Sales Execution: Actively drive the full customer lifecycle. Lead technical discovery and capability demonstrations during the pre-sales cycle, and remain deeply involved post-sale to guide implementation, ensuring agreed value and adoption thresholds are successfully reached.

• Hackathons & Prototyping: Think out of the box, have a „can-do“ attitude, and don’t shy away from complex operational problems. Leverage cutting-edge AI technologies to rapidly build creative prototypes in customer hackathons, solving critical pain points specific to power generation, transmission, and O&G operations.

• Agentic Process Transformation: Support our customers in achieving real ROI out of AI deployments at scale, enabling a fundamental shift from traditional, rule-based automation to the use of autonomous AI agents empowered by our Celonis Process Intelligence Platform (e.g., intelligent field service routing or autonomous procurement).

• Proof Projects: End-to-end execution of business-critical Proof-of-Value projects. This includes architecting and delivering secure, scalable LLM/agent systems with RAG, tools, and guardrails, while seamlessly integrating with enterprise data, identity protocols, and stringent energy compliance frameworks.

• Domain & Industry Leadership: Serve as the internal and external technical subject matter expert for the Energy vertical, scaling knowledge across the organization regarding utilities, upstream/downstream oil & gas, and renewable energy processes.

Requirements:

• 5+ years of experience leading technical pre-sales and post-sales engagements specifically within the Energy sector (Utilities, Oil & Gas, and/or Renewables). This includes defining AI roadmaps, building compelling ROI/TCO business cases, and guiding technical implementations through to value realization.

• Deep understanding of business processes native to the Energy sector (such as Asset Management, Field Services, Supply Chain, Capital Projects, or Trading) with the ability to translate high-level business needs into specific, impactful AI use cases.

• Expertise in generative AI techniques like RAG, few-shot learning, prompt engineering, multi-agent orchestration, multimodal understanding, or fine-tuning used to build high-impact use cases (e.g., automated processing of engineering documents, regulatory filings, or intelligent diagnostic chatbots).

• Solid knowledge of Python and common ML libraries (such as LangChain, pandas, pydantic, sklearn, PyTorch) as well as data engineering tools and technologies relevant to handling large-scale industrial data.

• Strong presentation skills to both internal and external stakeholders (including C-level executives and operational leaders), whether leading technical whiteboarding sessions or formal readouts and demos.

• Bachelor’s Degree required; Master's Degree in computer science, engineering, mathematics, or related fields, or equivalent work experience preferred.

Nice to have (big plus):

• Hands-on experience building agentic systems using LLM orchestration, RAG, function calling, and prompt engineering, while ensuring safety through rigorous evaluations and guardrails suited for highly regulated industries.

• Working knowledge of tools in the LLM ecosystem such as LangChain, LlamaIndex, or other OSS packages.

• Experience in deploying and monitoring models at scale across major cloud platforms (AWS Bedrock, Azure AI, GCP Vertex) and familiarity with IT/OT convergence and industrial IoT data structures.

Visa sponsorship is not offered for this role.
Learn More about Value Engineering at Celonis Here

Role Description

• As an Senior Value Engineer specializing in CPG and Fleet Logistics, you are pushing the envelope in solving business-critical problems for some of the world's most recognized consumer brands and global mobility networks. You will be working intimately with our most strategic enterprise clients, understanding their uniquely complex objectives—spanning from high-velocity beverage manufacturing and Direct Store Delivery (DSD) optimization to massive-scale fleet lifecycle management and dynamic asset routing. You will build Celonis solutions using the world’s leading Process Intelligence (PI) platform in combination with top AI and ML technology partners (e.g., Microsoft, OpenAI, Databricks).

  With Celonis’ Process Intelligence (PI) platform, we feed operational context to AI so it understands the intricate realities of our customers' massive supply chains, vehicle fleets, and distribution networks. This enables them to industrialize AI, unlocking real ROI on deployments at scale—ensuring products are always on the shelf and vehicles are always ready for the customer. There is no AI without PI. You will prototype these solutions, demonstrate their value to global Chief Supply Chain Officers (CSCOs) and Fleet Operations Executives, and ensure successful implementation, adoption, and value realization to solidify and expand the footprint of Celonis across these premier global networks.

Key Responsibilities:

• AI Discovery & Solutioning: Understand the clients' overarching AI strategies and the distinct supply chain challenges inherent to high-volume CPG and fleet mobility (e.g., predictive demand forecasting, route optimization, fleet predictive maintenance, dynamic asset allocation, and mitigating raw material volatility). As a Celonis product and logistics domain expert, translate these complex, physical-world challenges into innovative AI solutions that drive measurable impact.

• Pre- and Post-Sales Execution: Actively drive the full customer lifecycle. Lead technical discovery and capability demonstrations during the expansion/pre-sales cycle within various manufacturing, distribution, and fleet management business units. Remain deeply involved post-sale to guide implementation, ensuring agreed value, efficiency, and adoption thresholds are successfully reached.

• Hackathons & Prototyping: Think out of the box with a "can-do" attitude, tackling heavily siloed legacy logistics and telematics networks. Leverage cutting-edge AI technologies to rapidly build creative prototypes in client hackathons, solving critical pain points across inventory allocation, fleet routing, and DSD execution.

• Agentic Process Transformation: Support these enterprise clients in achieving tangible ROI from AI at scale. Enable a fundamental shift from traditional, rule-based automation to autonomous AI agents empowered by the Celonis Process Intelligence Platform (e.g., autonomous fleet rebalancing, intelligent shipment exception handling, or automated trade promotion deductions).

• Proof Projects: End-to-end execution of business-critical Proof-of-Value projects. Architect and deliver secure, scalable LLM/agent systems with RAG, tools, and guardrails, ensuring seamless integration with complex enterprise ERPs (e.g., SAP), Transportation Management Systems (TMS), Warehouse Management Systems (WMS), and massive IoT/telematics data streams.

• Domain & Industry Leadership: Serve as the internal and external technical subject matter expert for CPG and Mobility Supply Chains, scaling knowledge across the organization regarding the nuances of fast-moving consumer goods and asset-heavy fleet operations.

Requirements:

• 5+ years of experience leading technical pre-sales and post-sales engagements specifically within highly complex Supply Chain, CPG/Beverage, or Fleet Logistics environments. This includes defining AI roadmaps, building compelling ROI/TCO business cases for large-scale distribution networks, and guiding technical implementations through to value realization.

• Deep understanding of supply chain business processes native to high-volume manufacturing and mobility (such as Direct Store Delivery (DSD), Order-to-Cash, Procure-to-Pay, Fleet Lifecycle Management, or Sales & Operations Planning) with the ability to translate high-level operational needs into specific AI use cases.

• Expertise in generative AI techniques like RAG, few-shot learning, prompt engineering, multi-agent orchestration, multimodal understanding, or fine-tuning used to build high-impact use cases (e.g., intelligent dispatch assistants, or automated extraction of data from complex vendor contracts and logistics documentation).

• Solid knowledge of Python and common ML libraries (such as LangChain, pandas, pydantic, sklearn, PyTorch) as well as data engineering tools and technologies for handling massive, high-velocity transactional and IoT datasets.

• Strong presentation skills to both internal and external stakeholders (including supply chain executives, logistics directors, and enterprise IT leaders), whether leading technical whiteboarding sessions or formal readouts and demos.

• Bachelor’s Degree required; Master's Degree in computer science, supply chain management, logistics, engineering, mathematics, or related fields, or equivalent work experience preferred.

Nice to have (big plus):

• Hands-on experience building agentic systems using LLM orchestration, RAG, function calling, and prompt engineering, while ensuring safety through rigorous evaluations suited for enterprise-grade supply chain environments.

• Familiarity with CPG and fleet-specific systems (e.g., SAP APO/IBP, Blue Yonder, Manhattan, or major telematics/fleet tracking platforms).

• Working knowledge of tools in the LLM ecosystem such as LangChain, LlamaIndex, or other OSS packages.

• Experience in deploying and monitoring models at scale across major cloud platforms (AWS Bedrock, Azure AI, GCP Vertex).

Visa sponsorship is not offered for this role.
Learn More about Value Engineering at Celonis Here

Role Description

• As an Applied AI Engineer you are pushing the envelope in solving business-critical problems for our customers. You will be working with our most strategic customers, understanding their objectives and key challenges, and building Celonis solutions using the world’s leading Process Intelligence (PI) platform in combination with the largest AI and ML technology partners, such as Microsoft, OpenAI and Databricks. With Celonis’ Process Intelligence (PI) platform we feed operational context to AI so it understands our customers’ business and enables them to industrialize AI unlocking real ROI on AI deployments and at scale. There is no AI without PI. You will prototype these solutions, demonstrate their value to Executives and ensure successful implementation, adoption and value realization in order to increase the footprint of Celonis at those customers.

Key Responsibilities:

• AI Discovery & Solutioning: Understand customers AI strategy and business critical challenges. As Celonis product & domain expert, find the best problem-solution fit and translate customer requirements into innovative solutions that move the needle
• Hackathons & Prototyping: Think out of the box, have a „can-do“ attitude and don’t shy away from complex problems. Leverage cutting edge AI technologies to rapidly build creative prototypes in customer hackathons solving business critical problems
• Agentic Process Transformation: Support our customers in achieving real ROI out of AI deployments at scale enabling a fundamental shift in business operations from traditional, rule-based automation to the use of autonomous AI agents empowered by our Celonis Process Intelligence Platform
• Proof projects: End-to-end execution of business-critical Proof-of-Value projects, incl. architecture and deliver secure, scalable LLM/agent systems with RAG, tools, and guardrails; integrating with enterprise data, identity, and compliance frameworks.
• Ensure Successful Project Outcome: Applied AI Engineers stay involved with projects until agreed value & adoption thresholds are reached
• Specialize in Domains or Industries: To scale knowledge across the organization Applied AI Engineers specialize in domains (e.g. supply chain) and industries

Requirements:

• 7+ years of experience leading technical pre-sales, including defining AI roadmaps, building compelling ROI/TCO business cases and prototyping of machine learning and generative AI solutions.
• Understanding of generative AI techniques like RAG, few shot learning, prompt engineering, multi-agent orchestration, multimodal understanding, or fine-tuning that are used to build high-impact use cases like intelligent chatbots and automated text processors.
• Understanding of business processes across sectors (such as Supply Chain or Finance) with the ability to translate high-level business needs into specific AI use cases.
• Good knowledge of Python and common ML libraries (such as LangChain, pandas, pydantic, sklearn, PyTorch) as well as data engineering tools and technologies.
• Strong presentation skills to both internal and external stakeholders (including executives), whether whiteboarding sessions or formal readouts and demos.
• Bachelor’s Degree required, Masters Degree in computer science, engineering, mathematics or related fields, or equivalent work experience preferred.

Nice to have (big plus):

• Hands-on experience building agentic systems using LLM orchestration, RAG, function calling, and prompt engineering, while ensuring safety through rigorous evaluations and guardrails.
• Working knowledge of tools in the LLM ecosystem such as LangChain, LlamaIndex, or other OSS packages.
• Experience in deploying and monitoring models at scale across major cloud platforms (AWS Bedrock, Azure AI, GCP Vertex)

Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world. We are a technology and data driven group implementing a scientific approach to investing. Combining data, research, technology and trading expertise has shaped QRT’s collaborative mindset which enables us to solve the most complex challenges. QRT’s culture of innovation continuously drives our ambition to deliver high quality returns for our investors.

Your future role within QRT:

• Your core objective will be to provide high-touch user support to the global QRT team which trades across all the major EMEA and US markets.
• Provide prompt user support for investigations & resolution of any system issues impacting front office trading flows.
• Liaise with internal and external teams on a day-to-day basis to provide effective management of the QRT trading infrastructure, resolving issues through to completion.
• Support trading platforms on different OS environments – Windows & Linux (Manual trading application, Algorithmic trading platforms, Exchange/FIX connectivity system, and Risk systems).
• Ensure all trading components are up and running before the start of each trading day and support the release management process ensuring stability of the production environment.
• Provide maintenance and support of static databases and supporting processes.
• Develop tools to continuously improve our ability to monitor the trading environment.
• Evening/weekend support requirements (normally through remote access).

Your present skillset:

• >2yrs of Bash, Command Dos, Powershell or Python scripting experience is essential.
• Minimum 3yrs in a support role.
• Excellent problem solving and highly effective business analytics capabilities required.
• Must have a keen focus on ownership of issues and commitment to resolving them.
• Excellent written and oral communication skills are essential.
• Expert windows desktop and server administration experience is essential.
• High level of Linux OS experience is essential.
• Expert relational database skills (in order of preference: SQL Server, Postgres) are essential.
• Experience of managing the expectations of Front Office traders and senior management is desirable.
• Knowledge of Equities, Futures, Commodities and FX asset classes is an advantage.
• Knowledge of market data feed handlers and execution feeds is an advantage.
• Knowledge of market-making/liquidity provision and algorithmic trading is an advantage.

Base salary range for this position is $150,000 to $220,000 per year. 

QRT Total Compensation includes discretionary performance-based bonuses and a competitive benefits package.

About the role

Why this role is exciting

Why you'll love it here:

• 💰 Competitive Compensation. For this role our salary is targeted at $105-$140k USD per year. Final offer amounts are determined by factors such as experience and expertise. We take a geo neutral approach to compensation within the US, meaning that we pay based on job function and level, not location. 
• 🏆  Syndio Equity. So you can share in Syndio’s success.
• 🏝  Flexible Vacation Policy. We encourage our team to recharge when they need to, plus paid sick & safe time, compassion leave, parental leave, and voting leave. 
• 🩺  Medical, Dental, Vision. Syndio pays 90% of employee premiums, and 50% for dependents.
• 🏥  Life Insurance & Disability. Syndio covers the full premium.
• 🏦 401(k). To help you save for your future
• 📍 Remote-first in the following approved U.S. states: AZ, CA, CO, CT, DC, FL, GA, ID, IL, IA, MD, MA, MN, NE, NJ, NY, OH, OR, PA, RI, SC, SD, TX, UT, VT, VA, WA, and WI #LI-Remote

Role Progression:

In the 1st Month:

• Complete onboarding on the Syndio platform and the "Decision Intelligence" vision.
• Master the new Integration Platform and current data loading artifacts.
• Shadow 2-3 strategic client implementations and identify potential automation opportunities.
• Contribute to a pilot migration of an existing client’s data into the new platform.

At 3 Months:

• Independently lead technical implementations for new clients.
• Execute data migrations for a segment of the existing client base into the new Integration platform.
• Build reusable "Implementation Artifacts" (scripts, prompts, templates) that reduce time-to-value for the whole team.
• Contribute to the design of our new Platform’s Implementation process, identifying automation and other artifacts to minimize the client’s time to value.

By 6 Months:

• Manage a portfolio of high-complexity implementations for Syndio’s most strategic accounts utilizing automation and other artifacts to maximize client’s value in minimum time.
• Help achieve the goal of migrating 70% of the client base to the new Integration platform solution.
• Serve as a subject matter expert for "Process Embedding," ensuring clients are fully adopting the platform’s advanced setup features.

The interview overview

Below you'll find an outline of the interview plan for our Forward Deployed Engineer position. Please note that this is what we expect the process to look like; we may ask you for supplemental information or require an additional step before making a final decision.

1. 30 min phone call with a member of our Talent Team.
2. 45-60 min Zoom deep dive with the Hiring Manager (Director of Forward Deployed Engineering).
3. 75 min hands-on technical interview with a Senior Engineer.
4. 2-3 video interviews with key members of the Customer Success and Product teams.
5. 30 min interview with the SVP of Engineering - Data Strategy.

About Parloa

Parloa’s mission is to make every customer conversation feel effortless for both customers and the companies serving them. As agentic AI accelerates, Parloans are shaping the foundation of a new era in customer experience, one where customer support is no longer transactions, but meaningful exchanges. It is not just a vision; Parloa has powered over ONE BILLION interactions between global enterprise brands and their customers, with companies like Booking.com, HealthEquity, Allianz, SAP, BarmeniaGothaer, and TUI already deploying Parloa at scale. 

About the role:

As a Senior/Lead Forward Deployed Engineer (SFDE) at Parloa, you’ll be on the front lines of enterprise AI adoption - delivering cutting-edge technology into some of the most complex environments in the world. Your mission: lead end-to-end deployment projects from an engineering perspective - not just implementing what exists, but engineering what’s required to make it succeed at enterprise scale.

FDEs are our in-field product engineers. You will build what doesn’t yet exist, adapt architecture to enterprise realities, and engineer integrations across systems and APIs to make deployments successful. Your work is hands-on and code-first - you’ll be building, debugging, and engineering solutions directly in the field. What you ship not only makes deployments succeed, it also defines how customers experience Parloa and shapes what our product becomes.

This is not about maintenance. This is about engineering what makes it work in production, under real-world enterprise constraints, at speed and scale

Areas of ownership:

• Own deployment engineering projects: Lead the technical execution (incl. product engineering) of Parloa’s deployments inside large, complex enterprise environments
• Design for scale and resilience: Architect deployment solutions that meet enterprise-grade requirements for performance, reliability, and security
• Engineer solutions where none exist: Build custom extensions, integrations, and configurations to close product gaps and meet enterprise requirements
• Work across systems & stacks: Operate at the intersection of backend engineering, DevOps, and data engineering to ensure seamless delivery
• Partner with enterprise teams: Collaborate directly with customer engineering organizations to overcome constraints and deliver measurable outcomes
• Guide and mentor others: Collaborate with and provide technical guidance to more junior in-field engineers
• Influence at senior levels: Engage with enterprise architects and senior technical stakeholders, discussing solutions and architectures in depth to ensure alignment and credibility
• Debug under pressure: Rapidly unblock issues in mission-critical environments and ensure customer success.
• Shape the product: Act as the feedback loop between the field and our core engineering teams - turning deployment learnings into platform evolution
• Deliver long-term impact: Work closely with Deployment Strategists to ensure scalable architectures and durable customer value

Our Technology Stack: Python,TypeScript, Node.js, OpenAI, Microsoft Azure, Kubernetes, Docker, Terraform, MongoDB, MySQL, Redis, Kafka, MCP

Who you are:

• 7+ years of professional experience in software engineering, systems integration, DevOps, or data engineering roles with direct customer impact
• Proven track record of leading deep technical deployments and integrations in large-scale enterprise environments
• Strong technical depth across backend engineering, infrastructure-as-code, Kubernetes/cloud platforms, APIs, and databases - with proven experience in systems design and the ability to go hands-on in production systems
• Ability to operate independently in ambiguous, high-responsibility settings, especially when deploying live to enterprise systems
• A builder’s mindset with bias for action, creativity under pressure, and an obsession with making things work in the real world
• Degree in Computer Science, Engineering, or related technical field
• Strong plus: Experience building or integrating AI/LLM-powered systems (e.g., embedding agent workflows, vector databases, prompt orchestration)

Our recruiting process:

TA Screen → Hiring Manager Call → Technical Interview → Bar Raiser

Salary ranges include OTE

Why Parloa?

We’re at the beginning of a new era in customer experience, one where AI doesn’t just respond, but understands, reasons, and takes action. We’re building agentic AI that enterprises trust with their most important customer moments: complex questions, high volumes, real stakes. When millions of people reach out to a brand, those interactions aren’t just support tickets; they’re defining experiences. We’re here to raise the standard: making every conversation seamless, intelligent, and genuinely helpful. If you care about shaping how businesses and customers connect at scale—and want your work to matter in real, everyday moments—this is where you do it.

At Parloa, ownership isn’t a buzzword; it means being accountable for outcomes, not just tasks. We operate in a category that’s evolving fast, where the bar is high, and the problems are complex. We hire people who think in solutions, communicate with clarity, and follow through. People who are comfortable making decisions, taking responsibility, and raising the standard for themselves and those around them.

We’ve grown beyond the earliest startup phase, but we’ve kept the intensity: fast execution, direct feedback, and a strong expectation to contribute meaningfully from day one.

Backed by leading global investors like General Catalyst, EQT Ventures, and Altimeter Capital, we’re scaling with a clear ambition: to become the global category leader in enterprise-grade conversational AI.

About the Role, Mission or Department Overview

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

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

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

Responsibilities:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Basic Qualifications:

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

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

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

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

Preferred Qualifications:

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

#LI-Hybrid

REQ-019837

About the Team

The Developer Platforms organization provides cloud infrastructure and tooling to 100+ teams and 750+ engineers in NYT Product Engineering through an internal developer platform. The DevEx Platforms team (6 engineers) focuses on developer experience and workflow automation.

We’re now expanding into more AI-assisted developer workflows using tools like Claude Code, Cursor, and Copilot to support debugging, migrations, test generation, and everyday development in a safe, measurable way.

This role sits in Developer Platforms and reports to the Engineering Manager of the DevEx Platforms team.

About the Role

As a Senior Software Engineer on DevEx Platforms, you’ll build GenAI-augmented developer experiences and platform capabilities used across all of The New York Times.

Your work will help teams ship faster and more safely, pay down technical debt through structured migrations and platform modernization, and turn effective internal practices into reusable tools and agents.

What You’ll Do

• Design and build GenAI-powered developer tooling: prompts, IDE integrations, Cursor/Claude rules, and telemetry to understand how they’re used.
• Build and run the GenAI developer platform: Tools, services, and integrations leveraging LiteLLM to power both local and background/long-running agents, providing auth, configuration, and LLM routing.
• Own guardrails and observability for agentic workflows: integrate with Datadog, DX, and FinOut, define sensible standards, and make sure agents interact with Jira, GitHub, and internal platforms in a safe, auditable way.
• Work closely with engineers and managers across Product Engineering to understand how they build software today and to design solutions that actually fit the way they work.
• Support our mission and values, including journalistic independence, and keep ethics and safety central to how we use AI in developer workflows.
• This role includes limited on-call responsibilities; the schedule will be set when you join.Demonstrate support and understanding of our value of journalistic independence and a strong commitment to our mission to seek the truth and help people understand the world.
• You will report to the Engineering Manager of the DevEx Platforms team

Basic Qualifications:

• 5+ years of professional software engineering experience
• 2+ years of experience improving developer experience through workflows, documentation, and capabilities of the internal platform.
• Experience designing and building cloud native applications using Golang
• Hands-on experience building software using GenAI tools, like GitHub's Copilot, Cursor or Claude Code.

Preferred Qualifications:

#LI-Hybrid

REQ-018699

About the Role

The New York Times is hiring a Software Engineer to join the New A.I. Products & Platforms mission. We are a team building the next generation of reader-facing A.I. experiences for one of the world's most trusted news organizations.

We're looking for a prototyper: someone who moves fast, ships real things, and has a genuine instinct for what makes a product feel good. You'll work at the intersection of applied A.I. and consumer product. You'll turn emerging capabilities — language models, content embeddings, semantic search — into features that help Times readers discover, understand, and engage more deeply with our content. You'll be working inside an organization with a strong culture of editorial judgment and independence.

You need a track record of shipping, a strong command of the modern web stack, and real hands-on time building with LLMs in production.

This is a hybrid role based in our New York City headquarters, reporting to the VP of Engineering, New AI Products and Platforms. You can typically expect to come into the office 2+ days per week.

Responsibilities:

• Prototype, iterate, and ship LLM- and embedding-powered reader experiences from early concept through production, in conjunction with other engineers and cross-functional peers
• Build across the full stack: Node.js/TypeScript backends, React frontends, and A.I. integrations connecting them
• Build net-new A.I. features and integrate them into existing Times systems and APIs — knowing when to build from scratch and when to wire things together cleanly
• Write, test, and refine prompts and retrieval pipelines; treat prompt engineering as a first-class engineering discipline
• Instrument your features with evaluations, engagement signals, and performance monitoring, and use them to make things better
• Work closely with product managers, designers, journalists, and senior engineers to translate editorial goals into working product
• Contribute to shared practices around evaluation, responsible A.I. use, and what "good" looks like inside an organization where judgment and independence are taken seriously
• Demonstrate support and understanding of our value of journalistic independence and a strong commitment to our mission to seek the truth and help people understand the world

Basic Qualifications:

• 2+ years of software engineering experience with demonstrated ability to ship consumer-facing features
• Hands-on experience building with Large Language Models in a real product context — not just side projects
• Full-stack fundamentals: Node.js or TypeScript on the backend, React on the front end
• Experience navigating codebases and working within established backend infrastructure
• Proficiency in GenAI‑assisted developer tooling (e.g., Cursor, Copilot, or Claude Code) and passion for improving software delivery by leveraging these tools

Additional Qualifications:

• Experience working across both greenfield product development and integration with legacy or third-party systems
• Additional backend experience in Python, particularly for data pipelines or model integrations
• Experience with semantic search, embedding-based retrieval, or personalization systems
• Experience building agentic software — systems where models plan, take actions, or operate across multiple steps; complexity matters more than tenure
• Familiarity with evaluation frameworks or A/B testing for A.I. features
• Background in product environments where editorial or institutional judgment shapes what you ship

#LI-Hybrid

REQ-019883

AI Software Engineer

The Team + The Role

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

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

This role is based in our New York office.

What this looks like day-to-day

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

Who You Are

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

You're a builder, not a maintainer.

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

You're AI-curious - genuinely.

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

Must-haves

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

Nice-to-haves

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

About Pendo

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

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

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

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

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

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

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

Sr. AI Software Engineer

The Team + The Role

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

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

This role is based in our New York office.

What this looks like day-to-day

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

Who You Are

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

You're a builder, not a maintainer.

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

You're AI-curious - genuinely.

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

Must-haves

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

Nice-to-haves

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

About Pendo

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

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

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

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

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

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

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

Staff AI Software Engineer

The Team + The Role

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

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

This role is based in our New York office.

What this looks like day-to-day

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

Who You Are

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

You're a builder, not a maintainer.

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

You're AI-curious - genuinely.

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

Must-haves

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

Nice-to-haves

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

About Pendo

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

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

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

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

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

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

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

The role:

SoFi’s Staff AI Engineer is a highly experienced, hands-on individual contributor within SoFi’s growing independent risk organization, focused on owning the design, development, and evolution of agentic AI systems to solve real-world, high-impact problems.

This role will be instrumental in architecting, building, and scaling AI systems that enhance risk management and internal workflows, with a focus on creating reliable, reusable, and production-grade solutions.

This role operates at the intersection of the intelligence layer, including LLMs, agents, and orchestration, and the experience layer, which defines how users interact with and derive value from AI systems. You will shape how these systems are designed and integrated into critical workflows, ensuring they are intuitive, reliable, and effective in high-stakes risk environments.

You will work closely with the Senior Manager of AI Engineering as well as business stakeholders, to translate complex, ambiguous problems into scalable, production-grade AI systems with measurable impact.

What you’ll do: 

• Architect and Develop Agentic AI Systems: Lead the design and development of AI systems that leverage multi-step reasoning, tool use, and structured workflows, using frameworks such as LangGraph or similar approaches. Incorporate planning, memory, tool integration, and adaptive control flow to enable automated decisioning, risk insights, and internal platforms.
• Design the Experience Layer: Define how users interact with AI systems by designing workflows, interfaces, and feedback loops that drive adoption, usability, and trust. THis will involve close coordination with users / stakeholders. Ensure alignment between system behavior and user expectations.
• Context Engineering and System Design: Define and implement approaches for structuring inputs, outputs, and system context to improve reliability and performance of LLM systems, including prompt design, retrieval strategies, and workflow composition.
• Productionize AI Systems: Develop production-grade services and APIs, integrate agents into real systems, and ensure scalability, reliability, and maintainability.
• AI Observability and Evaluation: Build tracing, debugging, and evaluation frameworks to understand system behavior and continuously improve agent performance.
• Cross-Functional Collaboration: Partner with risk, engineering, and business teams to translate ambiguous problems into working AI systems and deliver measurable outcomes.
• Proof of Concepts and Innovation: Innovation and Prototyping: Identify high-impact opportunities to apply AI, rapidly prototype solutions, and evaluate emerging tools and approaches to inform long-term system design aligned with latest trends in AI.

What you’ll need:

• Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, or a related field.
• 7+ years of software engineering experience, with significant experience building and scaling AI-powered systems in production.
• Strong experience working with LLMs and building applications using prompting, APIs, and/or agent frameworks.
• Experience designing and implementing agentic systems, including patterns such as tool use, multi-step reasoning, and workflow orchestration.
• Deep experience in context engineering for LLM systems, including structuring inputs and outputs, prompt design, and retrieval-based approaches.
• Strong backend engineering experience, including building scalable services and APIs (Python preferred).
• Experience designing systems on cloud platforms such as AWS, Azure, or GCP, with an understanding of modern development and deployment practices.
• Experience working with structured and unstructured data, including building pipelines to support downstream AI applications.
• Experience defining and implementing evaluation frameworks for AI systems, including metrics, experimentation, and performance iteration.
• Strong system design skills, with the ability to architect scalable, reliable solutions.
• Ability to operate effectively in ambiguous problem spaces and translate them into well-defined systems.
• Strong communication and collaboration skills, with the ability to work cross-functionally and influence technical decisions.
• Demonstrated ownership mindset, with a track record of delivering high-impact systems end-to-end.

Nice to have:

• Experience designing and building user-facing workflows or internal tools powered by AI.
• Familiarity with observability and evaluation tools for AI systems such as Langfuse, LangSmith, or similar.
• Experience working in financial services or building systems for risk-related use cases.
• Experience with frontend technologies such as React for building AI-powered interfaces.
• Experience contributing to shared platforms, libraries, or internal tooling that enable reuse across teams.
• Experience building systems that require explainability, auditability, or operate in regulated environments.

This is Engineering at Lattice

At Lattice, we build software that helps people and organizations thrive. Our engineering team values maintainable systems, strong collaboration, and thoughtful product experiences.

As we expand AI-powered capabilities across our platform, security is a core part of how we design, build, and operate our product. Our Product Security team partners closely with engineering to ensure we ship features that are secure by default and resilient in production.

What You Will Do

You will work closely with product and platform engineering teams to improve the security of Lattice’s applications and services.

Product Security & Engineering Partnership

• Partner with engineers to identify, triage, and remediate security issues in product features and services
• Participate in security reviews and threat modeling for new features and systems
• Perform security-focused code reviews and help identify common vulnerabilities
• Contribute to secure-by-default patterns, libraries, and tooling in our TypeScript-based stack

Security Tooling & Operations

• Help implement and operate security tooling (SAST, DAST, dependency scanning, etc.)
• Support vulnerability management workflows, including internal findings and bug bounty reports
• Assist in investigating security issues and assessing risk and impact

Cloud & Platform Security

• Collaborate with platform and infrastructure teams to improve application and cloud security posture
• Contribute to improving security practices in AWS-based environments

AI/LLM Security

• Assist in identifying and mitigating risks in AI/LLM-powered features, including prompt injection, data leakage, and unsafe output handling
• Apply emerging best practices (OWASP Top 10 for LLM Applications) to real product use cases

Security Enablement

• • Contribute to security guidance, documentation, and training for engineering teams
  • Help improve how security is integrated into the development lifecycle

What You Will Bring to the Table

Experience that is important for you to have at some level:

• 1–3+ years of experience in product security, application security, or software engineering
• Experience writing and maintaining code in JavaScript/TypeScript (or similar languages like Python or Ruby)
• Familiarity with common web and API vulnerabilities (e.g., OWASP Top 10)
• Exposure to security testing tools (SAST, DAST, dependency scanning, etc.)
• Experience working in or with cloud environments (AWS or similar)

Technical Approach

• Ability to identify common security risks and suggest practical mitigations
• Understanding of secure coding practices and basic security controls
• Interest in how security decisions impact real-world product systems

Collaboration & Growth

• Strong communication skills and ability to work closely with engineering teams
• Willingness to ask questions, learn quickly, and take ownership of well-scoped problems
• Ability to contribute to team discussions and share security context effectively

Experience that would be nice to have:

• Experience with modern web architectures (Next.js, NestJS, GraphQL)
• Familiarity with containerization or Kubernetes
• Experience or interest in AI/LLM security, including

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The estimated annual cash salary for this role is $116,500 - $154,000. This position is also eligible for incentive stock options, subject to the terms of Lattice’s applicable plans

Benefits: The Company offers the following benefits for this position, subject to applicable eligibility requirements: Medical insurance; Dental insurance; Vision insurance; Life, AD&D, and Disability Insurance; Emergency Weather Support; Wellness Apps; Paid Parental Leave, Paid Time off inclusive of holidays and sick time; Commuter & Parking Accounts; Lunches in the Office; Internet and Phone Stipend; 401(k) retirement plan; Financial Planning; Learning & Development Budget

*Note on Pay Transparency:

Lattice provides an estimate of the compensation for roles that may be hired as required by state regulations. Compensation may vary based on (a) location, as Lattice factors in specific location when benchmarking compensation for most roles; (b) individual candidate skills and qualifications; and (c) individual candidate experience.

Additionally, Lattice leverages current market data to determine compensation, so posted compensation figures are subject to change as new market data becomes available. The salary, other compensation, and benefits information is accurate as of the date of this posting. Lattice reserves the right to modify this information at any time, subject to applicable law.

#LI-remote

Octus delivers breaking news and market-moving intelligence through cutting-edge data and technology for hedge funds, investment banks, and law firms- and we’re transforming how professionals access complex and opaque information. As part of our high-performing AI Innovation team, you’ll help design, build, and productionize modern GenAI and LLM-powered systems that support both client-facing features and internal operational efficiency. You’ll work end to end—from shaping ambiguous problems into scalable solutions to deploying reliable AI models in production - collaborating closely with product, engineering, and infrastructure teams. This is a hybrid role based in NYC (3 days in office per week). Curious about what we’re building? Check out our flagship GenAI product, CreditAI here and our AI framework here.

Responsibilities

• Apply strong problem-solving and critical thinking skills to break down complex, ambiguous requirements into clear, implementable technical components and system designs.
• Design, build, and maintain AI-powered and data-driven systems with a focus on modern language and multimodal models, including LLM-driven applications, RAG pipelines, and agentic workflows.
• Evaluate and productionize commercial and open-source LLMs, choosing appropriate models, tools, and techniques for each use case. Develop multi-step agentic workflows that incorporate tools, external data sources, memory, and control logic.
• Manage the orchestration of production LLM workflows and agentic systems, ensuring reliability and efficiency through prompt routing, state management, retries, fallbacks, and error handling. Design, test, and iteratively refine prompts and system instructions using prompt engineering and tuning techniques to improve model reliability, accuracy, and task performance.
• Maintain production-grade code and services with automated monitoring and performance tracking, using metrics and alerts to guide continuous improvements in models, prompts, and pipelines.
• Apply systems thinking to design and optimize AI and LLM systems, balancing quality, scalability, latency, cost, and operational complexity, while implementing efficiency improvements using model selection, prompt design, batching, caching, and retrieval strategies.
• Define and implement evaluation and observability frameworks for AI systems, including automated testing, task-specific benchmarks, regression testing for prompts, human-in-the-loop validation, and performance monitoring.
• Build and integrate AI models into backend systems and APIs to support both real-time and batch inference, ensuring solutions are production-ready, scalable, and efficient.
• Apply NLP and ML techniques to tasks such as information extraction, semantic search and retrieval, text classification, summarization, and reasoning over text and documents.
• Collaborate closely with engineering and infrastructure teams to deploy solutions using containerized and cloud-based environments (e.g., GitHub, Docker, AWS), applying modern deployment and infrastructure practices.
• Collaborate with product managers, business stakeholders, and domain experts to translate complex, ambiguous business problems into actionable technical solutions, and communicate progress, trade-offs, and outcomes to relevant stakeholders.
• Continuously learn and adapt to advancements in NLP and Generative AI to ensure solutions remain innovative and effective.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field (or equivalent practical experience).
• 3+ years of experience as a Data Scientist, Machine Learning Engineer, or applied AI practitioner, with a strong foundation in computer science, algorithms, and software development. 
• Advanced programming skills in Python, with experience building production-grade systems beyond research or experimentation.
• Solid understanding of machine learning and applied AI concepts, with experience taking solutions from prototype to production.
• Hands-on experience designing, building, and deploying LLM-driven or GenAI applications, including familiarity with vector databases, embeddings pipelines, or semantic search systems.
• Practical experience with cloud-based deployments and infrastructure tools (e.g., AWS, Docker, GitHub) and an understanding of modern DevOps practices, containerization, orchestration, caching strategies, and cost-aware design.
• Strong problem-solving skills and systems thinking, with the ability to balance trade-offs across model quality, scalability, inference latency, cost, and operational complexity.
• Ability to interpret and implement research ideas and algorithms, actively contributing to research and development initiatives while translating them into production solutions.
• Excellent communication and collaboration skills, with experience working closely with product managers, engineers, and domain experts to deliver actionable technical solutions.
• Passion for learning and staying current with the rapidly evolving AI/ML landscape, including emerging best practices for GenAI applications.
• Strong ownership and initiative, with the ability to independently drive projects from problem definition to delivery, while being a team player and contributing to the overall success of the data science team.

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

The salary range estimate for this position is $130,000-$145,000.

The actual compensation will be at Octus’ sole discretion and will be determined by the aforementioned and other relevant factors. This position is eligible for a performance-based annual bonus.

What we do  

You’ll join a software engineering team building internal AI solutions that support teams across The Trade Desk. We leverage large language models, retrieval‑augmented generation (RAG), agentic systems, and enterprise platforms like Microsoft Copilot and Anthropic Claude to improve productivity, streamline workflows, and reduce operational friction.

Our team also plays a key role in AI enablement across the company—supporting teams as they adopt Copilot, Claude and similar tools, designing and deploying custom AI agents, and guiding partners through their AI journey from idea to impact.

As a Senior AI Engineer on this team, you’ll be an end-to-end owner—collaborating closely with stakeholders, identifying opportunities, and delivering tools that make a real impact. We’re a nimble, fast-paced, and highly collaborative group that thrives on transforming the way teams operate and driving greater value across The Trade Desk.

What you’ll do: 

Some of the work that you will be doing to help us deliver on our mission is:

• Design and deploy intelligent agentic systems that integrate large language models (LLMs) with enterprise data, tools, and workflows using frameworks like LangChain, LlamaIndex, and Semantic Kernel.
• Develop Retrieval-Augmented Generation (RAG) applications using tools like Azure AI Search, vector databases, and secure enterprise connectors to deliver contextual insights.
• Build and deploy agents using Microsoft Copilot, Copilot Studio, Anthropic Claude, and similar platforms to help teams operationalize solutions within enterprise guardrails.
• Build and iterate on conversational agents that solve real-world problems, meet stakeholder needs, and deliver measurable business value.
• Deliver high-impact features by collaborating across teams, leading through ambiguity, and aligning technical solutions with business goals.
• Drive quality and performance through automated testing, monitoring, and data-driven evaluation of success criteria, user adoption, and operational efficiency.
• Mentor teammates and contribute to a culture of innovation, technical excellence, and continuous learning.

Who you are: 

• You have a Bachelor’s/Master’s level degree in computer science or relevant engineering-related field or equivalent experience.
• You have 8-10+ years of software engineering experience, including 1-2+ years working on AI-powered systems or products.
• You are proficient in Python and comfortable with additional languages such as C#, SQL, or TypeScript/React.
• You’re excited about AI enablement, including helping teams adopt platforms like Microsoft Copilot, Anthropic Claude and Copilot Studio, building agents, and guiding others through their AI journey.
• You have a good foundational knowledge of or hands-on experience with LLM orchestration frameworks (e.g., LangChain, LlamaIndex, Semantic Kernel) and prompt engineering using LLM APIs.
• You have experience productionizing applications and implementing CI/CD pipelines to ensure reliable, scalable deployments.
• You are skilled in data ingestion and transformation using APIs, ETL pipelines, and connectors, and familiar with vector databases and retrieval strategies.
• You communicate clearly across technical and non-technical audiences and thrive in a collaborative, cross-functional environment.
• You are a fast learner who adapts quickly to new technologies and solves complex problems with creativity and pragmatism.

#LI-TP1

A variety of technical opportunities is one of the best things about working at The Trade Desk as a software engineer, which is why we do not expect you to know every technology we use when you start. What we care about is that you can learn quickly and find solutions to complex problems using the optimum tools for the job. What you know is less important than how well you learn and innovate. We are not seeking engineers who know all the answers; we need engineers who can invent answers no one has thought of yet and find answers to the questions yet to be asked. 

The Trade Desk does not accept unsolicited resumes from search firm recruiters. Fees will not be paid in the event a candidate submitted by a recruiter without an agreement in place is hired; such resumes will be deemed the sole property of The Trade Desk. The Trade Desk is an equal opportunity employer. All aspects of employment will be based on merit, competence, performance, and business needs. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, veteran status, or any other status protected under federal, state, or local law.

[LA JOBS ONLY] The Trade Desk will consider qualified applicants with criminal histories for employment in a manner consistent with the requirements of the Los Angeles Fair Chance Initiative for Hiring, Ordinance No. 184652.

[SF JOBS ONLY] Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

#LI-TP1

Your Opportunity

Scion is paving a path in student living and the Facilities Manager is a keystone team member in the execution of our vision. This position ensures safety and functionality of the property’s buildings and equipment and ensures optimal service is carried out by the onsite Facilities team and any third-party vendors.

The Facilities Manager is a subject matter expert and experienced leader who excels in a fast-paced, agile, collegiate environment. This role demands superior problem solving, leadership and unwavering follow through. Those in the Facilities Manager position may also be asked to assist other properties.

Your Benefits

• FLSA Status Exempt
• Discretionary annual bonus
• Paid Time Off + Floating Holidays
• Health Insurance
• Dental Insurance
• Vision Insurance
• 401k Matching
• Paid Parental Leave
• Learning reimbursement opportunities

Your Responsibilities 

• Manage all building services and preventative maintenance programs to the best of industries practices, the Scion’s Standard Operating Procedures, and in compliance with the State/Federal Govt./OSHA’s Codes/regulations.
• Manage the day-to-day operations including but not limited to heating, ventilating, air conditioning (HVAC), plumbing and piping, fire protection, and general building maintenance and troubleshooting, building automation systems and building maintenance
• Monitor systems and provides diagnostics, preventative maintenance, annual testing, permitting, certification for all major building systems including, but not limited to: chilled water systems with fan coil units, air handling units with outside condensers including roof top units (RTU), building stationary boilers, hot & cold water distribution systems (plumbing, pressure/pneumatic/storage tanks, variable frequency drive (and pumps & motors), domestic hot water heaters, building automation systems (BAS), electrical distribution systems, switchgears and controls, generators, uninterrupted Power supply (UPS), elevators and lifts, parking structures (ventilation/drainage Systems, T-Joints), entrance & exit Gates and overhead doors, shingled/tarred roof, TPO membrane roofing system, EIFS, brick, vinyl, & cementitious sideboard, ACP, curtain walls, insulated glass units (IGUs).
• Operate and manage all life safety systems including fire pumps, fire alarm monitoring panels, dry/wet sprinkler systems, visual and audible alarms, back flow preventers, smoke detectors, CO2 sensors/alarms, fire extinguishers, emergency lighting, means of egress, fire
• Prioritize and schedule maintenance work, assuring quality of work consistent with Scion’s standards.
• Manage the Facilities Technicians to ensure all service requests are completed within Scion standards and are appropriately and accurately
• Manage facilities budgets.
• Review completed work to verify conformance to standards or repair
• Ensure all emergency, routine maintenance/repairs are carried out in a timely and professional manner.
• Ensure all Preventive maintenance is carried out in a timely and professional manner.
• Respond promptly and effectively to all safety/risk-management-related issues at the
• Review completed work orders for accuracy of labor hours, materials’ cost, and track recurring equipment failures.
• Conduct regular inspection tours of buildings, interior common areas and the exterior of the property, equipment and surrounding grounds for deficiencies, potential safety, health, or code problems and necessary
• Prepare written reports summarizing findings, and advises other building users as appropriate, of the
• Provide recommendations for dealing with problems noticed in
• Oversee and supervise “Turn” preparation including the prompt inspection of vacated units, makes appropriate repairs and charges, and ensures completion as
• Inspect vacant units after repairs and maintenance have been completed to determine quality of work performed and readiness for
• Manage the maintenance spare parts and the tools inventory, determining which parts/tools to stock and their stock level and working directly with General Manager to maintain inventory of parts, tools, and supplies within
• Maintain shop appearance and all mechanical, supply, electrical and storage facilities on
• Ensure proper maintenance of equipment to ensure warranty compliance and extended useful life.
• Oversee all other contracted work in the building, obtaining bids and negotiating prices with vendors and contractors, as approved by the General Manager, Regional Manager or the Regional Facility Manager.
• Coordinate deliveries, schedules, and performance of all facility related
• Ensure proper safety procedures are followed, personal protective equipment is used, and health & safety policies are
• Manage process for keying and coring/coding of locks and maintain lock procedures, logbooks and protocols for key management and
• Effectively implement standard operating procedures and preventive maintenance programs according to Scion’s
• Take part in emergency preparedness planning and acts as essential personnel in
• Escalate unique or urgent repairs/replacements to General Manager and Regional Facilities Manager.
• Maintain records on all major systems including maintenance manuals, manufacturer’s cut sheets, warranties, and equipment
• Work closely with the General Manager and Regional Facilities Manager to manage, formulate and track facilities and capital
• Hire, train and develop Facilities team members, holding them accountable for delivering a high level of

The responsibilities listed above may not be all-inclusive.

What We Require

• High school diploma/GED, trade school diploma, or military training
• 8 years of relevant experience managing building system in multi-unit residential properties, 5 years of supervisory experience
• 5 years’ experience with building automation systems, and life safety systems
• EPA 608 Universal certification
• HVAC/EPA 608 Universal certification
• Certified Pool Operator (CPO), or intention to obtain within six months
• Expertise in plumbing, HVAC, electrical and mechanical systems as well as generally accepted maintenance practices
• Boiler operator or Stationary Engineer Certificate/License
• Ability to read and interpret documents such as design drawings, blueprints, safety guidelines, operating and maintenance instructions and procedural manuals
• Ability to manage multiple projects simultaneously, set priorities and meet deadlines
• Basic computer working knowledge, including Microsoft Office and CMMS
• Valid driver’s license
• Ability to stand 8 hours or longer per day and lift up to 100 lbs. as necessary

 Operational Details

• Job location is at the assigned property. May be required to travel periodically.
• Serves in an “on-call” capacity, except during approved PTO periods.

The Scion Group LLC provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, religion, creed, national origin, color, gender, sex, sexual orientation, gender identity or expression, age, physical or mental disability (as long as the employee/applicant is otherwise qualified for the job with or without a reasonable accommodation), genetic information, HIV/AIDS status, marital status, uniformed service, veteran status, pregnancy or other legally protected status or category under federal or state law. The Scion Group LLC complies with applicable state and local laws governing nondiscrimination in employment in all locations in which the Company has properties. This policy applies to all terms and conditions of employment, including hiring, placement, promotion, termination, layoff, recall, transfers, leaves of absence, compensation, training and other terms and conditions of employment. The Scion Group LLC is committed to the principles of equal employment opportunities.

IND5 #wearehiring #werehiring

We're a new team building AI-assisted product experiences that help Datadog customers go from investigation to action, by enabling conversational workflows, guided remediations, and codefixes that address the root cause of production issues.

We're looking for a product-minded engineer to help us quickly define and ship applied AI experiences across chat, remediations, and codefixes. This role sits at the intersection of backend engineering, product development, and prompt engineering, with a strong emphasis on building reliable, production-quality AI systems.

At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them.

What You'll Do:

• Work closely with product managers, designers, and engineers to build and iterate on AI-powered product experiences in Bits AI SRE.
• Develop customer-facing systems across chat, remediations, and codefixes that help users resolve production issues more quickly.
• Work on prompts, evaluation loops, and backend systems to make applied AI workflows reliable, useful, and production-ready.
• Prototype quickly, test what works in the real world, and iterate rapidly to ship new product capabilities.
• Build the infrastructure and product logic needed to connect AI outputs to meaningful actions, including operational remediations and generated code changes.
• Collaborate with partner teams across Datadog to expand remediation capabilities and integrate with systems that support investigation, automation, and code generation.
• Follow the latest developments in LLM prompting, agent design, and applied AI product development, and bring strong judgment about what is practical to use in production.

Who You Are:

• You're an engineer with at least 5 years of professional experience, with strong backend engineering skills and a product mindset. You have experience building production systems in Go (or similar) and have worked with LLM-based systems in practice.
• You are excited about applied AI and motivated by building product experiences that help users take meaningful action, not just generate insights.
• You have experience with prompt engineering, evaluation, and iteration for LLM-powered systems, and know how to improve quality through experimentation and feedback.
• You have strong engineering fundamentals and can build the systems needed to productionize AI features, including integrating model behavior into reliable user-facing products.
• You are comfortable operating in a fast-moving environment with high ambiguity, and enjoy prototyping, learning quickly, and shipping early versions of new ideas.
• You collaborate well with cross-functional partners and can work effectively with product, design, and engineering to shape both the user experience and the technical implementation.
• You care about product quality and user outcomes, and can balance speed with pragmatism when building new AI-powered workflows.
• Bonus: you have experience with Kubernetes or systems related to production remediation and operational automation.
• Requirement - Demonstrated ability to use AI coding tools in day-to-day workflows and validate, critique, and refine AI-generated output.
• Plus - You’re motivated to push the boundaries of how AI can improve software engineering best practices and contribute to building AI-enabled products.

Datadog values people from all walks of life. We understand not everyone will meet all the above qualifications on day one. That's okay. If you're passionate about technology and want to grow your skills, we encourage you to apply.

Benefits and Growth listed above may vary based on the country of your employment and the nature of your employment with Datadog.

To conform to US export control regulations, candidates should be eligible for any required authorizations from the US government. This job is available in various departments within our company; to conform to US export control regulations, some of these roles may require candidates to be eligible for any required authorizations from the US government.

#LI-Hybrid

JOB SUMMARY

The Field Service Supervisor will be responsible for managing the field service engineers, helping to ensure our client’s onsite visits are proactive and filled with valuable work. This role reports to the Service Delivery Deputy Director responsible for service center. 

KEY RESPONSIBILITIES

• Help lead and prioritize the support team's workload.
• Visit our client sites at least 3 times per week and ensure that our engineer’s work product is aligned with clients and CTS expectations. 
• Liaise with internal teams, vendors, and client contacts. 
• Act as a resource to the rest of the team for rapid decisions and direction as well as help to coordinate escalations.
• Help identify areas where Standard Operating Procedures (SOP's) don't exist or need further work. In addition, to help improve those processes.
• Regularly monitor the teams' overall performance and report on metrics to the CM’s, SA’s, and SDM’s. Participate in efforts to improve different level engineer group performance (e.g. manage dashboards, SLA’s, KPI’s. risks/issues).
• Be a liaison to clients where it relates to service delivery issues.
• Ensure that proactive work is being carried out by engineers in addition to the day-to-day service requests/incidents). 
• Identify areas of needed coaching and training for the team.  
• Listen to the teams overall internal and client facing feedback and help resolve any conflicts and issues..
• Manage overall expectations of our clients via phone, email, tickets or any other method approved and used by CTS.
• Ensure the team follows established procedures for routine work like creating new users, configuring laptops/desktops, NVR’s, PBX’s, and phones.
• Ensure the prompt and thorough documentation of all work by the team via our ticketing system, ITGlue and other tools. This includes supporting the team with documenting assets, configurations, passwords, processes, etc and maintaining current and accurate documentation.
• Ensure the consistency and efficiency of our documentation efforts. Support the team with documenting assets, configurations, passwords, processes, etc. and maintaining current and accurate documentation.
• Keep track of supervised engineers' efforts, their successes and failures and provide acknowledgement where there is success as well as support and direction where there is failure.
• Review onsite engineers EOD reports to ensure quality of work and compliance with our standards  
• Review all supervised engineers ticket communication where time allows but definitely on a weekly basis
• Work closely with the Client Strategy teams and also participate in the preparation of the QBRs (Quarterly Business Review) when requested in order to assist with managing the relationship and service offerings to the client. 
• Have 1 on 1’s with supervised engineers and provide clear metrics of their overall performance. Lastly, ensure engineers are fulfilling expected output hours and quality of work. 
• Provide onsite assistance when a client is experiencing frustration with our overall services. 
• Identify & Lead Initiatives to Improve on all KPIs & Performance Metrics alongside the SDM.
• Accountable to KPIs & Metrics that will include things such as Client Satisfaction Scores (CSAT), SLA’s, Team Efficiency, Team Happiness as well as intangible metrics that align with things such as our Company Core Values

REQUIRED SKILLS AND QUALIFICATIONS

• 5+ years of experience in a customer-facing, technical-services environment, with a strong commitment to customer service. 
• Great communication skills, founded in being a good listener who seeks to understand the position of those being supported.
• Experience in Scheduling and Resource Management.
• Experience in networking protocols: TCP/IP, BGP, OSPF, VPN, IPSEC, VoIP and QoS.
• Experience with WAN/LAN networking including routing, switching, security and load balancing.
• Experience in WLAN technologies including, but not limited to, Meraki, UniFi and Cisco.
• Experience with Microsoft Windows Server, Windows Desktop and Apple Ecosystems.
• Experience in Network Attached Storage (NAS). 
• Experience with Grand Stream or other Asterisk based PBX systems.
• Experience with Android, iOS, Gsuite and Office365.

PREFERRED SKILLS AND QUALIFICATIONS

• Experience working for a Managed Service Provider (MSP) or IT Support Business.
• Experience with MSP-related tools: ConnectWise, Kaseya VSA, PRTG, and ITGlue.

LOCATION

This is a full-time mainly onsite position in the New York Metro area with the ability to work remotely some days per week (dependent on client needs). Some night and/or weekend work may at times be required. Lastly travel to other states (although rare) may be required as we expand to other states. 

COMPENSATION

The annual salary range for this role is $100,000 to $120,000.

BENEFITS

• Competitive compensation
• Health Insurance (medical, vision, dental), 80% covered for employee-only plans and 75% covered for employee-spouse, employee-kids, and employee-family plans
• Flexible Spending Account (FSA)
• Health Savings Account (HSA)
• Employee Assistance Program (EAP)
• Retirement Plan (401(k)) with company match
• Commuter Benefits
• Short-Term Disability Insurance fully paid by the company
• Long-Term Disability Insurance fully paid by the company
• Life and AD&D Insurance, with optional Supplemental Life Insurance
• Paid Time Off, including Paid Parental Leave
• 10 Holidays
• 2 Floating Holidays

CTS participates in the E-Verify Program. As part of this program, the company provides the federal government with your Form I-9 information to confirm your employment eligibility in the United States.
Learn more at www.e-verify.gov (information available in English and Spanish).

About the Role:

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

What You'll Do:

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

Who You Are:

Minimum Qualifications

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

Preferred:

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

Why CoreWeave?

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

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

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

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

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive.

Every day, millions of riders and drivers depend on Lyft — and when something goes wrong, we want to make it right as fast as possible. The Self-Serve Intelligence team within Safety & Customer Care org is building the AI-powered systems that do exactly that: resolving rider and driver suboptimal experiences without agent involvement through intelligent solutions in AI Assist (e.g. AI Agents), automations, and self-serve workflows. We're building backend systems, APIs, and AI-powered products that fundamentally rethink how customers get help — combining robust engineering with GenAI to deliver reliable, scalable self-serve experiences.

As a Senior Software Engineer on the Self-Serve Intelligence team, you will design, build, ship, and maintain backend services and AI-powered products (e.g. various AI Agents) that autonomously resolve customer suboptimal experiences. You'll own the full lifecycle — from system design and prototyping through production deployment and operational excellence — partnering with product, design, data science, and operations to deliver high-quality, reliable solutions at scale.

Responsibilities:

• Design, build, ship, and maintain scalable backend services and GenAI-powered products (e.g. AI Agents) that resolve rider and driver suboptimal experiences autonomously
• Develop robust evaluation frameworks to measure and improve the quality and impact of GenAI-driven customer experiences
• Lead projects from idea to production, owning end-to-end system design, implementation, and operational health
• Write well-crafted, well-tested, readable, maintainable code
• Design and evolve APIs, data models, and service architectures that support rapid product iteration
• Rapidly adopt and integrate emerging AI capabilities to accelerate development velocity across the team
• Incorporate considerations for edge cases, failure modes, and business context into system design
• Participate in code and architecture reviews to ensure quality and distribute knowledge
• Own system reliability — monitor, debug, and improve service health, latency, and availability
• Proactively participate in resolving ongoing incidents
• Unblock, support, and effectively communicate across teams to achieve results
• Share your knowledge by giving brown bags, tech talks, and evangelizing engineering and AI best practices

Experience:

• 5+ years of software engineering industry experience
• Strong backend engineering fundamentals — designing, building, and operating distributed systems and microservices in production
• Hands-on experience with LLMs, agentic workflows, or building AI Agents — we value depth of AI experience and treat it as a strong differentiator
• Track record of building, evaluating, and shipping GenAI or ML-powered products
• Experience defining API schemas and developing backend services in a microservices environment
• When facing an ambiguous problem or outside your expertise, you can quickly learn what you need to dig in, make sense of it, and start working toward a solution
• You're a great communicator who can advocate for your proposals while empathizing with your teammates' goals and priorities
• You have a history of going out of your way to help your fellow engineers be more productive

It would be a huge plus to have:

• Strong proficiency in Python
• Experience with AI agent frameworks, prompt engineering, or model evaluation pipelines

Benefits:

• Great medical, dental, and vision insurance options with additional programs available when enrolled
• Mental health benefits
• Family building benefits
• Child care and pet benefits
• 401(k) plan with company match to help save for your future
• In addition to 12 observed holidays, salaried team members have discretionary paid time off, hourly team members have 15 days paid time off
• 18 weeks of paid parental leave. Biological, adoptive, and foster parents are all eligible
• Subsidized commuter benefits
• Monthly Lyft credits and complimentary Lyft Pink membership

Lyft is an equal opportunity employer committed to an inclusive workplace that fosters belonging. All qualified applicants will receive consideration for employment without regards to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, age, genetic information, or any other basis prohibited by law. We also consider qualified applicants with criminal histories consistent with applicable federal, state and local law.

Lyft highly values having employees working in-office to foster a collaborative work environment and company culture. This role will be in-office on a hybrid schedule — Team Members will be expected to work in the office 3 days per week on Mondays, Wednesdays, and Thursdays. Lyft considers working in the office at least 3 days per week to be an essential function of this hybrid role. Your recruiter can share more information about the various in-office perks Lyft offers. Additionally, hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year. #Hybrid

The expected base pay range for this position in the San Francisco area is $185,000 - $222,000, not inclusive of potential equity offering, bonus or benefits. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive.

Machine Learning is at the heart of Lyft’s products and decision-making. Machine Learning Engineers at Lyft operate in dynamic environments, moving quickly to build the world’s best transportation solutions. We tackle a wide range of challenges, from pricing and marketplace frameworks that ensure reliability and competitiveness, to agentic AI platforms that automate analytical workflows, to behavioral detection systems that protect the integrity of our network. We operate at the intersection of applied ML and real business impact, shipping models that directly influence revenue, rider experience, and partner trust.

Lyft Business builds products that help organizations move the people who matter most—employees, customers, patients, and guests—easily and efficiently. Our offerings include Business Travel, Lyft Pass, and Concierge (for healthcare and non-healthcare rides), enabling companies to manage transportation at scale through APIs, integrations (e.g., Concur, Expensify), and dedicated tools. These platforms power high-impact B2B use cases across corporate travel, healthcare access, customer experience, and community programs.

We're looking for a Machine Learning Engineer to design, build, and deploy ML systems across Lyft Business. This is a high-scope role: you won't be siloed into one problem area. Instead, you'll move across pricing algorithms, fraud and behavior detection, agentic AI systems, and emerging ML applications as the business evolves. You'll write production-quality code, own models end-to-end from prototyping through deployment, and collaborate closely with Data Scientists, Product Managers, and Software Engineers to translate complex business problems into scalable ML solutions.

This role is ideal for someone who is technically versatile, energized by variety, and wants to see their work directly shape a large-scale business.

Responsibilities:

• Develop and deploy ML models across multiple problem domains — including dynamic pricing, marketplace optimization, fraud detection, and anomaly/behavior detection — in production environments serving millions of rides
• Build and iterate on agentic AI systems (e.g., LLM-powered analytical agents) that automate decision-making and reduce operational overhead
• Design and implement feature pipelines, model training workflows, and serving infrastructure using Lyft's ML platform
• Partner with Data Scientists on the Algorithms and Decisions teams to take research prototypes from proof-of-concept to production at scale
• Evaluate ML system performance against business KPIs, run experiments, and drive continuous model improvement
• Identify new opportunities where ML can create leverage across Lyft Business verticals (Healthcare, Lyft Pass, Business Travel) and pitch solutions
• Contribute to team engineering standards — code quality, observability, documentation, and testing practices

Experience:

• Experience with GenAI / LLM ecosystems — prompt engineering, RAG, agent frameworks (e.g., LangChain, LangGraph), or fine-tuning
• Exposure to graph-based ML methods (graph neural networks, knowledge graphs, network analysis)
• Experience with pricing, marketplace, or fraud-related ML problems
• Familiarity with cloud ML services (AWS SageMaker, Bedrock) or internal ML platforms
• Track record of identifying and scoping ML projects independently, not just executing on pre-defined specs

Benefits:

• Great medical, dental, and vision insurance options with additional programs available when enrolled
• Mental health benefits
• Family building benefits
• Child care and pet benefits
• 401(k) plan with company match to help save for your future
• In addition to 12 observed holidays, salaried team members have discretionary paid time off, hourly team members have 15 days paid time off
• 18 weeks of paid parental leave. Biological, adoptive, and foster parents are all eligible
• Subsidized commuter benefits
• Monthly Lyft credits and complimentary Lyft Pink membership

Lyft is an equal opportunity employer committed to an inclusive workplace that fosters belonging. All qualified applicants will receive consideration for employment without regards to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, age, genetic information, or any other basis prohibited by law. We also consider qualified applicants with criminal histories consistent with applicable federal, state and local law.

Lyft highly values having employees working in-office to foster a collaborative work environment and company culture. This role will be in-office on a hybrid schedule — Team Members will be expected to work in the office 3 days per week on Mondays, Wednesdays, and Thursdays. Lyft considers working in the office at least 3 days per week to be an essential function of this hybrid role. Your recruiter can share more information about the various in-office perks Lyft offers. Additionally, hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year. #Hybrid

The expected base pay range for this position in the San Francisco area is $176,000-$211,200, not inclusive of potential equity offering, bonus or benefits. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.

The Product Solutions Architecture (PSA) team acts as a technical multiplier across Datadog. PSAs are domain experts who partner with Field teams on complex customer use cases across pre- and post-sales engagements and scale their impact by producing reusable collateral, including reference architectures, technical guides, and enablement assets. By feeding real-world customer insights back to Datadog Product teams, PSAs help influence product roadmaps while accelerating adoption, usage, and long-term customer success.

Datadog’s LLM Observability product enables organizations to monitor, troubleshoot, and optimize large-scale LLM-powered applications with confidence, while meeting requirements around data privacy, compliance, and cost management. As a Product Solutions Architect, you will partner closely with Datadog customers and the LLM Observability product team to design architectures, implement best practices, and drive adoption of LLM observability across customer environments.

At Datadog, we place value in our office culture - the relationships and collaboration it builds, and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them.

What You’ll Do:

• Serve as the in-house subject matter expert for Datadog’s LLM Observability product
• Partner with Field teams to provide hands-on technical and architectural guidance to enterprise customers adopting LLM Observability
• Create high-impact technical collateral, including reference architectures, technical guides, cookbooks, and documentation to enable Field teams and the broader customer community
• Build proofs of concept and small-scale deployments to validate solutions and reproduce real-world customer environments
• Act as a trusted advisor to Product Management by delivering actionable feedback informed by real-world field experience

Who You Are: 

• You bring a strong software engineering foundation, with hands-on experience building and operating LLM-powered applications in production environments, and are comfortable diving deep into codebases to understand architectural decisions, tradeoffs, and implementation details
• You are familiar with distributed systems and core observability concepts, such as tracing, metrics, and logging, and can translate those patterns into the emerging LLM observability domain, including LLM spans, evaluations, and prompt templates
• You have experience with languages such as Python and/or JavaScript/TypeScript
• You are comfortable operating in rapidly evolving, ambiguous technical domains
• You build deep context across teams and translate it into reusable, scalable solutions
• You take ownership from problem definition through implementation and measurable outcomes
• You are highly detail-oriented, particularly when working on architectures and customer-facing technical assets
• You bring strong listening and consultative skills and are experienced supporting customers in high-pressure, complex situations
• Able to sit up to 4 hours, traveling to and from client sites 
• Able to travel via auto, train, or air up to 40% of the time 

Bonus Points:

• Successful track record with 5+ years experience working as an Architect or Consultant 
• Experience using Datadog and/or other observability tools in an SRE or DevOps capacity
• Experience with LLM application evaluation and testing frameworks

Datadog values people from all walks of life. We understand not everyone will meet all the above qualifications on day one. That's okay. If you’re passionate about technology and want to grow your skills, we encourage you to apply.

Benefits and Growth: 

• Best-in-breed onboarding
• Generous global benefits
• Intra-departmental mentor and buddy program for in-house networking
• New hire stock equity (RSUs) and employee stock purchase plan (ESPP)
• Continuous professional development, product training, and career pathing
• An inclusive company culture, able to join our Community Guilds and Inclusion Talks

Benefits and Growth listed above may vary based on the country of your employment and the nature of your employment with Datadog.

We're looking for a Staff Software Engineer with deep experience in AI/ML to join the Notebooks team. Datadog Notebooks provide a flexible tool for ad-hoc data analysis, collaborative investigations, and technical documentation. Its fluidity makes it an ideal surface for agents and humans to interact. The Notebook team is building AI-driven workflows for natural-language data analysis, assisted graph generation, automated investigations, and more. You will act as a technical leader in this domain, focused on building industry-leader AI-powered features. We’re looking for a product-minded ML engineer with strong technical expertise, excellent communication skills, and a track record of driving impactful initiatives end to end.

At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them.

What You’ll Do:

• Spearhead AI initiatives within Notebooks, serving as the technical owner for the design, development, and deployment of ML/AI-driven features.
• Shape the long-term technical direction and strategy for AI workflows within Notebooks and related products.
• Champion the full development cycle: from rapid prototyping and defining success metrics/evaluations to running experiments, iterating, and achieving production-ready scale and reliability.
• Drive product direction with a strong user focus, always advocating for the best end-user experience.
• Coordinate with other leaders and product teams interested in leveraging Notebooks in their AI-powered workflows.
• Develop robust agent systems, including tools, retrieval and planning strategies, guardrails, prompt management/evaluations, fallbacks, and human-in-the-loop processes.
• Stay up to date with industry trends, guiding the adoption of new technologies and evolving best-practices.

Who You Are:

• You have a BS/MS/PhD in a scientific field or equivalent experience
• 10+ years of relevant engineering experience, as well as experience acting as a technical lead
• Proven track record of leading large-scale AI/ML initiatives in a product-driven environment
• You have significant experience in model deployment, development, training, fine-tuning, and evaluation

• You are passionate about mentoring others and cultivating a high-performing engineering culture.
• You’re excited about leveraging AI tools to enhance how you code, solve problems, and build

Datadog values people from all walks of life. We understand not everyone will meet all the above qualifications on day one. That's okay. If you’re passionate about technology and want to grow your skills, we encourage you to apply.

Benefits and Growth:

• Generous and competitive benefits package
• New hire stock equity (RSUs) and employee stock purchase plan
• Continuous career development and pathing opportunities
• Employee-focused best in class onboarding
• Internal mentor and cross-departmental buddy program
• Friendly and inclusive workplace culture
  
  

Benefits and Growth listed above may vary based on the country of your employment and the nature of your employment with Datadog.

#LI-Hybrid

The ML Observability team builds cutting-edge tools to monitor, explain, and improve AI systems in production, particularly those leveraging Large Language Models (LLMs) and generative AI. We provide robust, scalable observability for AI workloads, including drift detection and model evaluation, and behavior tracing, enabling customers to ship AI with confidence.

As a Staff Engineer, you’ll lead the development of new features and foundational capabilities within Datadog’s LLM Observability product. You will shape product direction, drive experimentation, and apply your deep understanding of both AI systems and software engineering to solve open-ended problems in the fast-moving AI landscape. Your work will directly impact how our customers monitor, troubleshoot, and optimize LLM-based applications in production.

Join us in building the foundational tools that make AI systems observable, understandable, and reliable in the real world.

At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them.

What You’ll Do:

• Drive design and implementation of LLM observability features.

• Ideate, prototype, and scale new product features to provide insights and drive improvements for generative AI systems
• Work cross-functionally with other eng teams, product, UX, and applied science to iterate fast and find product-market fit
• Develop and extend tools for tracing, evaluating, and debugging LLMs
• Influence architecture decisions and mentor engineers to build resilient, high-performance systems
• Stay close to customer pain points and use those insights to guide product and engineering priorities
• Stay current with industry trends and advancements in machine learning and observability, driving innovation within the team

Who You Are:

• You have a BS/MS/PhD in a Computer Science, Engineering or related scientific field or equivalent experience
• Deep understanding of distributed systems and scalable backend architectures
• Hands-on experience building and shipping LLM-powered or GenAI applications. 
• Understanding of model internals, inference pipelines, evaluation techniques, and prompt engineering
• Ability to thrive in ambiguous, fast-changing spaces and have a product-oriented mindset
• You’re excited to shape the next generation of AI observability tools from the ground up
• Communicate clearly, think rigorously, and take pride in clean, maintainable code
• Experience with observability tools/platforms

Datadog values people from all walks of life. We understand not everyone will meet all the above qualifications on day one. That's okay. If you’re passionate about technology and want to grow your skills, we encourage you to apply.

Benefits and Growth:

• Get to build tools for software engineers, just like yourself. And use the tools we build to accelerate our development.
• Have a lot of influence on product direction and impact on the business .
• Work with skilled, knowledgeable, and kind teammates who are happy to teach and learn
• Competitive global benefits
• Continuous professional development

Benefits and Growth listed above may vary based on the country of your employment and the nature of your employment with Datadog.

#LI-Hybrid

ABOUT THE ROLE:

As a Sr. Full Stack Engineer (AI) at Rightway Healthcare, you'll spearhead our AI integration initiatives to revolutionize healthcare delivery through our platform. Working with a dynamic team, you'll drive the development of AI-powered features that empower members to make informed healthcare decisions, ensuring they get the right care at the right time and price. This role offers a unique blend of autonomy and innovation, where you'll transform cutting-edge AI concepts into production-ready solutions. While AI is a key focus, you'll also architect and implement solutions that scale across our platform, combining intuitive technology with expert guidance to bring clarity and direction to our members' healthcare journeys.

WHAT YOU’LL DO:

• Pioneer AI-driven solutions that enhance our care navigation and PBM services, directly improving healthcare outcomes for our members
• Lead rapid prototyping cycles, transforming concepts into production features within days or weeks, not months
• Architect and implement full-stack solutions using our modern tech stack (Node.js, React, TypeScript, Ruby, GraphQL, Postgres)
• Drive technical decisions independently while collaborating with cross-functional teams to ensure alignment with business goals
• Build and maintain scalable, production-grade systems that handle sensitive healthcare data with reliability and security
• Establish technical standards and best practices for AI integration across the platform
• Mentor team members and contribute to our engineering culture of innovation and excellence

WHO YOU ARE:

• A versatile full-stack engineer with 5+ years of experience building production applications at scale
• A self-driven problem solver who thrives in ambiguous environments and creates structure from chaos
• An innovative thinker who balances quick experimentation with production-quality implementation
• A technical leader comfortable making independent decisions while valuing team collaboration
• An effective communicator who can translate complex technical concepts for diverse audiences
• A pragmatic engineer who values shipping working solutions over perfect solutions
• Someone passionate about using technology to solve real healthcare challenges

EXTRA CREDIT:

• Experience designing and implementing AI/ML features in production environments
• Proven track record of optimizing LLM implementations and prompt engineering
• Background in healthcare technology or experience with HIPAA compliance

SALARY (BEFORE BONUS POTENTIAL):  $175,000 - $190,000 + bonus + equity

CYBERSECURITY AWARENESS NOTICE

In response to ongoing and industry-wide fraudulent recruitment activities (i.e., job scams), Rightway wants to inform potential candidates that we will only contact them from the @rightwayhealthcare.com email domain. We will never ask for bank details or deposits of any kind as a condition of employment. 

ABOUT RIGHTWAY:

Rightway is on a mission to harmonize healthcare for everyone, everywhere. Our products guide patients to the best care and medications by inserting clinicians and pharmacists into a patient’s care journey through a modern, mobile app. Rightway is a front door to healthcare, giving patients the tools they need along with on-demand access to Rightway health guides, human experts that answer their questions and manage the frustrating parts of healthcare for them.

Since its founding in 2017, Rightway has raised over $130mm from investors including Khosla Ventures, Thrive Capital, and Tiger Global at a valuation of $1 billion. We’re headquartered in New York City, with a satellite office in Denver. Our clients rely on us to transform the healthcare experience, improve outcomes for their teams, and decrease their healthcare costs.

HOW WE LIVE OUR VALUES TO OUR TEAMMATES:

We’re seeking those with passion for healthcare and relentless devotion to our goal. We need team members that embody our following core values:

1) We are human, first
Our humanity binds us together. We bring the same empathetic approach to every individual we engage with, whether it be our members, our clients, or each other. We are all worthy of respect and understanding and we engage in our interactions with care and intention. We honor our stories. We listen to—and hear—each other, we celebrate our differences and similarities, we are present for each other, and we strive for mutual understanding.

2) We redefine what is possible
We always look beyond the obstacles in front of us to imagine new solutions. We approach our work with inspiration from other industries, other leaders, and other challenges. We use ingenuity and resourcefulness when faced with tough problems.

3) We debate then commit
We believe that a spirit of open discourse is part of a healthy culture. We understand and appreciate different perspectives and we challenge our assumptions. When working toward a decision or a new solution, we actively listen to one another, approach it with a “yes, and” mentality, and assume positive intent. Once a decision is made, we align and champion it as one team.

4) We cultivate grit
Changing healthcare doesn’t happen overnight. We reflect and learn from challenges and approach the future with a determination to strive for better. In the face of daunting situations, we value persistence. We embrace failure as a stepping stone to future success. On this journey, we seek to act with guts, resilience, initiative, and tenacity.

5) We seek to delight
Healthcare is complicated and personal. We work tirelessly to meet the goals of our clients while also delivering the best experience to our members. We recognize that no matter the role or team, we each play a crucial part in our members’ care and take that responsibility seriously. When faced with an obstacle, we are kind, respectful, and solution-oriented in our approach. We hold ourselves accountable to our clients and our members’ success.

Rightway is PROUDLY an Equal Opportunity Employer that believes in strength in the diversity of thought processes, beliefs, background and education and fosters an inclusive culture where differences are celebrated to drive the best business decisions possible. We do not discriminate on any basis covered by appropriate law. All employment is decided on the consideration of merit, qualifications, need and performance.

Engineering at Brex

Engineering at Brex is about building systems that scale with speed and intention. Our teams span Software, Data, Security, and IT, and operate with high autonomy and deep collaboration. We tackle hard technical problems, own our outcomes, and push for excellence at every level — from architecture to deployment. It’s an environment where engineering is a craft, and builders become leaders.

What you’ll do

As an Application Security Engineer, you will contribute to finding and responding to security vulnerabilities across the Brex platform. In this role, you will participate in code reviews, design reviews, penetration testing, and vulnerability management. You will contribute to tooling that performs static and dynamic testing of the Brex platform and supports secure developer workflows. Application Security is part of our wider Financial Scale organization, which means you will work closely with Security Operations, GRC, Product Security, Front End Platform, and IT Infrastructure teams.

We're looking for individuals with a solid foundation in penetration testing and a curiosity for finding vulnerabilities in complex systems. You should have experience identifying and documenting vulnerabilities across common vulnerability classes and be able to communicate their risk clearly to engineering and product teams. This role is highly cross-functional — you'll have the opportunity to collaborate with engineering teams across Brex and grow your security expertise in a fast-moving environment.

Brex is pioneering the next wave of AI-driven financial services for dynamic, high-impact companies like Coinbase, Robinhood, and Anthropic. As we integrate AI across our product suite, this role will give you hands-on experience contributing to AI security at Brex. You'll apply emerging AI security best practices to help secure our agentic features, identify attack surfaces introduced by LLM-powered systems, and partner with product and engineering teams to build AI capabilities our customers can trust with their critical financial operations.

Responsibilities

• Identify vulnerabilities across common vulnerability classes (e.g., OWASP Top 10), document findings clearly, and communicate risk to drive remediation efforts
• Participate in penetration testing and design reviews alongside senior engineers, contributing to the identification of vulnerabilities and insecure designs
• Contribute to internal tooling and automation efforts that support SAST and DAST testing of the Brex platform and promote secure development practices
• Collaborate with engineering and product teams to support the design of secure product features
• Actively contribute to a culture of security awareness through knowledge sharing and peer learning

Requirements

• 4+ years of work experience in Application Security or a related role
• Demonstrated ability to find and document vulnerabilities in complex systems, with clear communication of business risk
• Hands-on experience with a subset of secure development activities, such as code review, threat modeling, or penetration testing
• Experience identifying security risks in AI/ML systems — such as prompt injection, model manipulation, or data poisoning — through work experience, personal projects, CTFs, or bug bounties
• Familiarity with agentic workflows and the ability to reason about attack surfaces introduced by LLM-powered features
• Knowledge of Python or scripting languages to automate tasks and build tooling
• Collaborative mindset paired with strong written and verbal communication skills

Bonus points

• Experience with Kotlin, gRPC, GraphQL, Kubernetes
• Previous experience as a software engineer
• Experience with securing distributed systems in AWS and cloud environments
• Experience with web application security reviews
• Contributions to the wider technical community — open source, public research, CTF participation, blogging, CVEs, or presentations
• Experience submitting to bug bounty or responsible disclosure programs
• Published AI security research or contributions to AI security frameworks

Compensation

The expected salary range for this role is $152,000 - $190,000. However, the starting base pay will depend on a number of factors including the candidate’s location, skills, experience, market demands, and internal pay parity. Depending on the position offered, equity and other forms of compensation may be provided as part of a total compensation package.

Brex LLC is a wholly owned subsidiary of Capital One, N.A.

CAIS is the pioneer in democratizing access to and education about alternative investments for independent financial advisors, empowering them to engage and transact with leading asset managers on a massive scale through a wide variety of alternative investment products and technology solutions. CAIS provides financial advisors with a broad selection of alternative investment strategies, including hedge funds, private equity, private credit, real estate, digital assets, and structured notes. CAIS also delivers industry-leading technology, operational efficiency, and world-class client service throughout the pre-trade, trade, and post-trade experience. CAIS supports over 50,000 advisors who oversee more than $6 trillion in network assets.

The AI-Forward IT Support Engineer is the visible face of IT at CAIS headquarters. This role owns the day-to-day support experience—managing the support team, serving as the senior escalation point, and making sure employees can get things done. Alongside that, this person is a genuine practitioner of AI tools, using them to manage, maintain, and engineer solutions in the Microsoft ecosystem.

The AI dimension here is practical and people-facing: comfort with tools like Claude and Codex, the ability to get employees access to what they need, and the enthusiasm to show someone how to write their first useful prompt. The goal is to be the person who makes AI feel accessible, not intimidating.

Key Responsibilties 

Pillar 1 — IT Support Leadership & Microsoft Estate

• Support Team Management: Manage CAIS’s support function firmwide, leveraging internal staff and MSP resources. Hold partners accountable for performance and drive continuous improvement in service quality.
• Senior Escalation Point: Act as the senior escalation point for all end-user support issues across the organization. Own complex or unresolved tickets through to resolution, ensuring no issue falls through the cracks between the support team and internal stakeholders.
• Microsoft Environment Management: Lead day-to-day administration and engineering of the CAIS Microsoft estate: Intune device policies and compliance profiles, Azure / Entra enterprise application management, and M365 service health. Beyond break-fix—this includes proactive configuration, policy improvement, and keeping the environment clean and well governed.
• Visible Face of IT: Be physically present and accessible at CAIS HQ. Build trust with employees as the go-to person when something isn’t working—whether that’s a support issue or a question about technology. Represent IT in a way that is responsive, human, and credible.

Pillar 2 — AI Operations & End-User Enablement

• AI Tooling Comfort: Use Claude Code and OpenAI Codex as primary tools for engineering solutions in the Microsoft environment—generating and refining scripts, automating repetitive admin tasks, troubleshooting configurations, and improving documentation. AI tooling is how this person works, not an afterthought.
• AI Access Operations: Ensure CAIS employees have access to the AI tools they need. Manage provisioning, permissions, and basic configuration for end-user AI platforms. Be the first point of contact when someone can’t get in, doesn’t know where to start, or needs a tool set up.
• On-Ramp & Evangelism: Help colleagues take their first steps with AI tooling. Run informal sessions, share practical tips, introduce basic prompt engineering concepts, and be available for one-on-one guidance. The goal is not deep training—it’s making the tools feel approachable and getting people to their first win as quickly as possible.

Required

• Experience managing a support function—internal team, MSP, offshore, or a combination; comfortable holding vendors and partners accountable to service standards
• Strong hands-on knowledge of Microsoft 365, Azure / Entra ID, and Intune—able to own configuration and policy work, not just administer tickets
• Comfortable daily user of Claude Code, OpenAI Codex, or equivalent AI tooling—applied to real engineering and administrative tasks
• Excellent communication and interpersonal skills—comfortable engaging at all levels of the organization and able to explain technology clearly to non-technical users in plain, practical terms

Preferred

• Financial services industry experience or a similar fast-paced, entrepreneurial environment

Qualifications

• 5+ years of progressive IT experience
• 2+ years leading a support function or small team
• Demonstrated experience using AI tools (e.g., Copilot, ChatGPT, Claude)
• Hands-on expertise in automating Microsoft 365, Azure, and Intune environments

You may be a good fit if you:

• Have significant software engineering experience, particularly with distributed systems
• Are results-oriented, with a bias towards flexibility and impact
• Pick up slack, even if it goes outside your job description
• Enjoy pair programming (we love to pair!)
• Want to learn more about machine learning systems and infrastructure
• Thrive in environments where technical excellence directly drives both business results and research breakthroughs
• Care about the societal impacts of your work

Strong candidates may also have experience with:

• High-performance, large-scale distributed systems
• Implementing and deploying machine learning systems at scale
• Load balancing, request routing, or traffic management systems
• LLM inference optimization, batching, and caching strategies
• Kubernetes and cloud infrastructure (AWS, GCP, Azure)
• Python or Rust

Representative projects:

• Designing intelligent routing algorithms that optimize request distribution across thousands of accelerators
• Autoscaling our compute fleet to dynamically match supply with demand across production, research, and experimental workloads
• Building production-grade deployment pipelines for releasing new models to millions of users
• Integrating new AI accelerator platforms to maintain our hardware-agnostic competitive advantage
• Contributing to new inference features (e.g., structured sampling, prompt caching)
• Supporting inference for new model architectures
• Analyzing observability data to tune performance based on real-world production workloads
• Managing multi-region deployments and geographic routing for global customers

Deadline to apply: None. Applications will be reviewed on a rolling basis. 

Position Summary

Arcesium is seeking a Software Engineer to join our Forward Deployed (FD) team within Client and Partner Development. This role is distinct from traditional product engineering: our mission is time to value. Forward Deployed Engineers rapidly design and deliver solutions that directly address client business needs while building reusable foundations that make the next project faster and better.   

This role prioritizes autonomy, execution, and impact. You won’t be limited to incremental features on a fixed roadmap; you’ll help define the problems, shape the solutions, and deliver outcomes that matter to both clients and the product. Your work will power client relationships, accelerate adoption of Arcesium’s platform, and shape the evolution of our products. Forward Deployed Engineers sit in a uniquely leveraged position: seeing client problems first, solving them end-to-end, and turning those solutions into lasting capabilities for the platform.  

What Makes This Role Unique  

• Time-to-value is the mission.  Solutions must be delivered quickly while building strategic infrastructure that accelerates future work. 
• You work directly with clients.  FD Engineers are part of the face of Arcesium and partner closely with client technology owners and business stakeholders.  
• You close the loop with product.  You will identify patterns across client needs and translate them into improvements, features, and platform direction.  
• You build targeted, high-impact solutions.  Expect many small, self-contained deliverables that directly solve business problems alongside foundational components that endure.  
• Code lives at multiple horizons.  Some solutions run for years in production; others exist briefly to unlock immediate value.

Responsibilities

• Own execution and delivery of client-facing technical solutions, integrations, and data workflows. 
• Gather requirements, iterate on deliverables, and drive projects through completion with client technology owners, client executives, and Arcesium stakeholders. 
• Design and build data pipelines, integrations, and targeted applications that address specific business needs. 
• Collaborate with platform and product engineers on architecture, patterns, and reusable capabilities informed by client work. 
• Rapidly prototype and deliver proof-of-concepts that demonstrate value and inform longer-term solutions. 
• Contribute feedback and real-world use cases to product teams to influence roadmap and platform evolution. 
• Operate across environments: your solutions may run in Arcesium’s platform, client environments, or someone’s laptop. 

Qualifications

• 5+ years delivering data-centric technical solutions to enterprise-scale customers is preferred, but what we’re really looking for is someone with the drive to solve real problems on time, drive innovation, and make yourself and the people around you better every day. You will need to take ownership through ambiguity and connect technical work to business outcomes. If that sounds like you, come show us what you can do – we would love to have you. 
• Experience across software engineering, solution delivery, and client collaboration. 
• Strong programming skills. We use Java, Python, TypeScript, and SQL. 
• Ability to balance speed and quality, building both rapid solutions and durable systems. 
• Experience with cloud platforms and the modern data stack is a plus. 
• Familiarity with financial services, investment lifecycle, or asset management is a plus. 
• Experience using AI in enterprise products and systems, both internal and client-facing, is a plus. 

The expected annual base salary for this position is $200,000 - $240,000 Base. Our compensation package includes variable compensation in the form of a year-end bonus, guaranteed in the first year of hire, benefits including medical and prescription drug coverage, and 401k contribution matching.

The applicable annual base salary paid to a successful applicant will be determined based on multiple factors including without limitation the nature and extent of prior experience and educational background.  #LI-JS1 #LI-Hybrid

Remote eligible states include: CA, CT, DE, FL, GA, IL, MA, MN, NJ, NY, NC, OH, PA, TX, VT, WA

Arcesium's Personal Data Privacy Notice for Candidates is linked here. 

Deadline to apply: None. Applications will be reviewed on a rolling basis.