Staff ML Software Engineer (L6) — Platform Systems, AIMS Engineering

At Netflix, our mission is to entertain the world. Together, we are writing the next episode - pushing the boundaries of storytelling, global fandom and making the unimaginable a reality. We are a dream team obsessed with the uncomfortable excitement of discovering what happens when you merge creativity, intuition and cutting-edge technology. Come be a part of what’s next.

About the Job

AI for Member Systems (AIMS) runs the AI systems behind every recommendation, search result, and personalized experience for 300M+ members. The stack powering it is large and battle-tested, built to meet the demands of its time, and remarkably effective at doing so. But AI/ML is moving fast, and the infrastructure that got us here needs to evolve to meet what's next: new model paradigms, tighter cost and efficiency expectations, and the operational maturity that comes with running AI at this scale. Migrating to a next-generation AI/ML platform is one of the highest-leverage programs in AIMS. So is building the observability and cost infrastructure that makes that platform trustworthy. This role owns that problem end-to-end.

Platform Systems is the engineering foundation of AIMS, owning reliability, scalability, cost efficiency, and developer experience across the org. We are looking for a Staff ML Software Engineer to own the technical health of the AIMS AI/ML stack — modernizing it, and building the observability and cost infrastructure that makes that modernization trustworthy. This is a high-leverage, cross-cutting role — the work you do here will define how AIMS builds AI/ML systems for the next decade. While the initial migration marks our first major initiative, our ongoing goal is to establish sustainable practices for the long term.

Responsibilities

• Define the end-state architecture for the modernized AIMS AI/ML stack: how it is organized, what contracts each layer exposes, and what the migration path looks like across training pipelines, AI frameworks, and data infrastructure

• Drive end-to-end migration of AIMS AI/ML systems onto a modern, Python-native platform, coordinating across multiple AIMS teams and external platform partners, with dozens of production models in flight

• Build migration tooling and shared abstractions that reduce the cost of adoption for individual teams, so modernization does not require each team to solve the same problems independently

• Own scalability across training throughput and data pipelines, ensuring AIMS AI/ML systems stay performant as model complexity and member traffic grow

• Design and build observability systems that give AIMS AIMS ML practitioners deep visibility into model behavior, training pipeline health, serving latency, and data quality, making issues detectable and diagnosable before they become incidents

• Identify and drive cost optimization across AIMS training and serving infrastructure, developing frameworks and tooling that make compute efficiency a first-class concern, not an afterthought

• Architect reliability improvements across the AIMS AI/ML stack, reducing toil, improving on-call ergonomics, and setting the standard for operational excellence across the org

• Prototype and productionize GenAI-powered tooling for anomaly detection, root cause analysis, and operational automation, applying LLM-based systems to the problems of AI/ML reliability and cost at scale

• Surface systemic cost, reliability, and migration gaps by embedding with AI/ML teams across AIMS, and translate their friction into concrete engineering investments with org-wide leverage

• Set technical standards for the modernized stack and raise the engineering bar across AIMS through design reviews, architectural guidance, and leading by example

• Own the long-term architectural evolution of the AIMS AI/ML stack — continuously evaluating emerging infrastructure patterns, model paradigms, and platform capabilities, and translating them into a forward-looking roadmap before they become urgent migrations

What We're Looking For

• Significant experience designing, building, and operating large-scale production AI/ML systems, including training pipelines and familiarity with model serving and online inference at high-traffic scale

• Hands-on experience migrating production AI/ML systems across technology generations; you have done this before and understand where it goes wrong

• Strong software engineering fundamentals with deep Python expertise and working proficiency in at least one JVM language (Scala or Java)

• Proven track record of improving AI/ML system reliability, reducing infrastructure costs, and improving operational scalability

• Experience building observability and monitoring systems for AI/ML workloads; you understand what good visibility looks like across training, serving, and data pipelines

• Strong distributed systems background, including large-scale batch processing and real-time serving infrastructure

• Collaborate with partner teams to drive cross-functional technical programs, setting direction, managing dependencies, and building consensus without formal authority

• High technical judgment: able to identify common patterns, build reusable frameworks, and make pragmatic calls on what to migrate, what to rewrite, and what to leave alone

• Comfortable operating without full information; you can scope a problem, define an approach, and course-correct as you learn more

Preferred Qualifications

• Experience with compute and cost optimization for AI/ML workloads at scale, including capacity management and efficiency tooling

• Hands-on experience building GenAI-powered tooling for operational automation, root cause analysis, or anomaly detection in AI/ML systems

• Experience building developer tooling or platform abstractions that improve AI/ML practitioner velocity

• Applied experience in personalization domains such as recommendation systems, search, or discovery

• Familiarity with modern AI/ML infrastructure patterns including feature stores, model serving platforms, and experiment frameworks

Generally, our compensation structure consists solely of an annual salary; we do not have bonuses. You choose each year how much of your compensation you want in salary versus stock options. To determine your personal top of market compensation, we rely on market indicators and consider your specific job family, background, skills, and experience to determine your compensation in the market range. The range for this role is $600,000.00 - $1,066,000.00.

Netflix provides comprehensive benefits including Health Plans, Mental Health support, a 401(k) Retirement Plan with employer match, Stock Option Program, Disability Programs, Health Savings and Flexible Spending Accounts, Family-forming benefits, and Life and Serious Injury Benefits. We also offer paid leave of absence programs. Full-time hourly employees accrue 35 days annually for paid time off to be used for vacation, holidays, and sick paid time off. Full-time salaried employees are immediately entitled to flexible time off. See more details about our Benefits here.

Netflix is a unique culture and environment. Learn more here.

Inclusion is a Netflix value and we strive to host a meaningful interview experience for all candidates. If you want an accommodation/adjustment for a disability or any other reason during the hiring process, please send a request to your recruiting partner.

We are an equal-opportunity employer and celebrate diversity, recognizing that diversity builds stronger teams. We approach diversity and inclusion seriously and thoughtfully. We do not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender, gender identity or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service.