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About This Role
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 next challenge for Netflix Games will be applying AI at the scale and quality consumers expect to make real impact. Come be a part of what’s next. We're seeking a technical leader to help shape the strategy, development \& delivery of GenAI tools and agentic systems across Netflix Games. This is a pivotal role that will drive how AI creates real benefit and support across the organization, needing a leader who can work on product strategy, internal alignment, and bring state\-of\-the\-art engineering hands\-on execution to building agentic systems and machine learning capabilities.
Key Responsibilities
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- Builder PM: Identify key opportunities where AI can add value to teams, defining the product strategy and architecture of new enablement systems.
- Derive insights from user research and quantitative data to create product roadmaps, which identify the highest return investments, distinguish hype from value.
- Work across diverse teams in game studios and platforms, to inform and advise on existing initiatives, as well as how new tools will fit within the broader ecosystem.
- Prototyping to Shipping : Bring product strategy skills and builder capabilities to every initiative, define what's worth building, not just how to build it. Prototype fast to validate ideas with real systems, identify where agentic AI genuinely transforms player experience or team productivity, and take features from concept to production without a handoff layer.
- Work with technologies across the game organization stack, from game engines, to services, to Agentic systems (Claude Code, Cowork, OpenClaw, custom agents and bots)
- Contribute to engineering production\-grade agentic systems: multi\-step reasoning pipelines, tool\-use agents, multi\-agent orchestration, and autonomous workflows. Design and build the code harnesses and scaffolding connecting frontier models (or open\-weight alternatives) to tooling, game engines, and platform APIs.
- Build reusable agent primitives and infrastructure , MCPs (Model Context Protocols) and shared agentic libraries , that raise the floor for the whole organization and reduce duplicated effort across game studios and platform teams.
- Where needed, iterate on model capabilities through model fine\-tuning, DPO to align outputs with quality preferences, LoRA/QLoRA for efficiency, and RLHF for long\-horizon agentic tasks. With the aim of improving overall tools performance.
- Data Driven Decision Making: Define AI evaluation as a first\-class discipline, including defining overall product evaluations strategy, curating offline eval sets, automated scoring pipelines, and regression gates. Define and build online evaluation: A/B testing, production telemetry, user feedback loops, and anomaly detection for agent behavior drift.
- Partner with external researchers, developers, and companies pioneering work in the agentic AI space. Stay on the forefront of emerging frameworks, open models, and infrastructure patterns, and bring those learnings back to accelerate our own efforts.
- Collaborate with our engineering and platform teams to build robust solutions and scale core capabilities: model inference, data pipelines, responsible AI compliance, safety guardrails, and graceful degradation at scale.
- Managing a small team of engineers
Qualifications
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We're looking for a technical leader who can help shape the strategic direction for agentic systems across Netflix Games , and a hands\-on builder of those systems.
- 7\+ years of experience in AI product strategy, machine learning, and AI engineering with a strong hands\-on engineering foundation.
- 3\+ years of experience in the game development industry.
- Product management experience, identifying user needs, defining product roadmaps, running production development workstreams.
- Experience in games or interactive entertainment, shipping game features, working in game engines (Unreal, Unity), or building AI experiences for players.
- Deep, practical experience building and deploying agentic AI systems in production, multi\-step reasoning, tool use, multi\-agent orchestration, or autonomous workflow automation.
- Strong Python engineering skills and production experience with agentic frameworks (LangChain, LangGraph, AutoGen, Google ADK, or equivalent).
- Proven experience designing and operating evaluation infrastructure for AI systems , offline benchmarks, automated scoring pipelines, and online experimentation.
- Deep understanding of the Gen AI ecosystem , open models, data requirements, infrastructure and tooling, safety frameworks, and the trade\-offs between hosted and in\-house solutions.
- Comfortable navigating from research prototypes to production\-ready systems in a fast\-paced, cross\-functional setting, in partnership with engineering teams, AI research, and game studios.
- Strong communicator who can convey complex AI system design to technical and non\-technical stakeholders, and build alignment across game studios, platform teams, and leadership.
Nice to Have
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- Familiarity with Model Context Protocol (MCP) or similar agent\-to\-system connectivity standards.
- Experience with inference optimization for agentic deployments: latency reduction, cost management, streaming responses.
- Background in responsible AI: safety evaluation, prompt injection defense, output moderation, and content policy compliance.
- Experience with the full LLM fine\-tuning lifecycle: SFT, DPO, LoRA/QLoRA, and RLHF for long\-horizon tasks. Comfortable taking a fine\-tuning project from dataset curation through deployment and ongoing maintenance.
- Published research or public contributions in agentic systems, RLHF, or LLM evaluation.
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 $890,000\.00 \- $1,690,000\.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.
Salary Context
This $890K-$1690K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Netflix, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($1290K) sits 490% above the category median. Disclosed range: $890K to $1690K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Netflix AI Hiring
Netflix has 5 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, Research Engineer. Positions span Los Gatos, CA, US, Remote, US. Compensation range: $640K - $1690K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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