ML Software Engineer 6 - AI for Member Systems (AIMS)

ML Software Engineer 6

• AI for Member Systems (AIMS) Netflix
• 3.9 Los Gatos, CA Job Details $600,000
• $1,066,000 a year 8 hours ago Benefits Stock options Paid holidays Health insurance Flexible spending account Paid time off 401(k) matching Qualifications AI models Systems integration Software engineering Requirements design Integration Architecture Design (Architecture design skills) Production systems Collaboration with product development teams Model deployment Enterprise Integration Developing large-scale AI models Prototype creation Model training Prototypes MLOps Generative AI Cross-functional communication Full Job Description 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. The Opportunity At Netflix, our mission is to entertain the world by connecting members to a vast library of global stories. With over 300 million members in 190+ countries, our product helps people quickly find something great to watch. The AI for Member Systems (AIMS) organization sits at the core of this experience, building and operating the AI systems that power recommendations, personalization, search, discovery, and messaging. AIMS is developing a foundational new architecture, building a shared intelligence layer that unifies how personalization works across every Netflix surface. This layer orchestrates foundation models, retrieval, ranking, and policy through a governed runtime, replacing fragmented surface-by-surface systems with a coherent platform that compounds learning across the entire member experience. We are looking for a senior ML Software Engineer (L6) to work at the intersection of this intelligence layer and the platform infrastructure that supports it. This role focuses on the systems that connect

AIMS' AI

capabilities to the broader Netflix ML and serving ecosystem, ensuring they are scalable, well-integrated, and built for the next generation of AI-powered member experiences. Responsibilities Design and build the platform integration layer between AIMS' intelligence systems and Netflix's ML infrastructure, serving layer, and product engineering teams Drive architectural decisions on how AIMS capabilities (ranking, retrieval, orchestration, foundation models) connect to shared platform services including model serving, inference infrastructure, feature stores, and experiment platforms Own the technical design of key integration points between the intelligence layer and the member experience serving stack, ensuring clean contracts, reliable handoffs, and operational excellence Architect "paved paths" for AIMS application teams to build and deploy ML models and GenAI capabilities through consistent patterns in data access, training, evaluation, deployment, and monitoring Design reusable, horizontal infrastructure components that multiple AIMS teams can adopt, avoiding duplication and ensuring improvements propagate across surfaces Scope and de-risk new architectural directions through prototypes and proofs of concept, especially where optimal abstractions between the intelligence layer and platform infrastructure are not yet clear Shape requirements with platform and infrastructure partners, translating AIMS' needs into actionable capabilities and ensuring architectural alignment with broader Netflix platforms Drive technical excellence across the systems you touch, raising the bar on reliability, observability, performance, and cost-effectiveness Partner closely with ML practitioners (research scientists, research engineers) to ensure platform and infrastructure decisions support rapid model development and experimentation Mentor and elevate engineers across AIMS through technical guidance, design reviews, and championing principled engineering practices What We're Looking For Significant experience designing, building, and operating production ML systems end-to-end, spanning data access and pipelines, training and evaluation workflows, model serving, and online inference for high-traffic products Strong software engineering fundamentals with deep Python expertise, plus working proficiency in a JVM language (Scala or Java) to build and integrate with large-scale platform services Experience building platform and integration layers that connect ML capabilities to shared infrastructure (serving, inference, feature/embedding stores, experimentation), with clean APIs/contracts and reliable operational handoffs Demonstrated ability to work at the boundary between ML teams and platform/infrastructure teams, translating requirements in both directions and driving alignment Fluency with modern ML and GenAI patterns, including recommendation/ranking systems, LLM serving, and agentic architectures Proven ability to identify common patterns and design frameworks and abstractions that are flexible, extensible, and easy for engineers to adopt Hands-on ability to scope and validate architectures through prototypes, turning ambiguous problems into concrete proposals and reference implementations Comfortable influencing technical direction across multiple teams without formal authority, building consensus and guiding complex trade-offs Strong communication skills with the ability to operate as a technical partner across organizational boundaries Thrives in a fast-moving, high-autonomy environment with significant ambiguity Preferred Qualifications Experience designing orchestration runtimes and agentic architectures that coordinate multiple ML capabilities behind a unified, governed interface Experience building or evolving the integration layer between ML systems and member-facing serving infrastructure, including clean contracts, rollout strategies, and operational ownership Strong distributed systems background, including large-scale real-time and batch processing Applied experience in personalization domains such as recommendation systems, search, or discovery, with familiarity using major ML frameworks (TensorFlow, PyTorch, or JAX) Proven ability to shape architecture in ambiguous environments, including cost efficiency, capacity planning, and compute optimization for ML workloads 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.