Interested in this AI/ML Engineer role at Netflix?
Apply Now →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 Games Technical Program Management (TPM) team, an integral part of Games Engineering, is dedicated to developing robust, end\-to\-end platform capabilities for developers and enabling Netflix members to seamlessly discover and engage with exciting interactive experiences. Our work spans game canvases across TV, web, and mobile; the Netflix Games Controller; on\-device and cloud gaming infrastructure; and the studio tools and developer ecosystem that power every title we ship.
Data is foundational to how we build, launch, and improve games at Netflix. Our Games Data Science \& Engineering (DSE) teams build the measurement infrastructure, experimentation frameworks, telemetry pipelines, and analytical capabilities that inform decisions across both the Games Platform and our internal and external game studios. As Games scales, the complexity and criticality of this data infrastructure grows in lockstep.
As a TPM embedded in this space, you will drive the programs that make Games DSE work, from telemetry pipelines and experimentation platforms to studio\-facing analytics tools and cross\-functional data initiatives. You will sit at the intersection of engineering execution and analytical insight, partnering with data scientists, data engineers, platform engineers, and game studio teams to ensure the data systems underpinning Netflix Games are reliable, scalable, and impactful.
Who You Are
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- An experienced technical program manager with a track record of delivering complex, multi\-stakeholder programs in data engineering, data science, or analytics\-adjacent domains.
- Technically fluent enough to engage meaningfully with data engineers and scientists, you understand data pipelines, telemetry architectures, experimentation frameworks, and analytical tooling without needing everything translated for you.
- A systems thinker who can map dependencies across platform, studio, and cross\-functional data teams, and drive clarity when the path forward is ambiguous.
- A strong communicator who can write crisp program updates for engineers and readable executive summaries for leadership, and knows when each is needed.
- Proactive about risk: you identify problems before they become blockers and bring solutions, not just status.
- Collaborative by default, but direct when decisions need to be made or priorities need to be contested.
- Committed to an inclusive working environment where people from all backgrounds can do their best work.
What You Will Do
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### Drive Games Data Platform Programs
- Own end\-to\-end program delivery for Games Platform DSE initiatives, including telemetry instrumentation, data pipeline development, metrics infrastructure, and the systems that power game quality and performance measurement.
- Partner closely with data engineers, platform engineers, and product teams to build and ship foundational data capabilities that serve games across TV, mobile, web, and cloud.
- Manage cross\-functional dependencies between Games Platform engineering and Games DSE, ensuring teams are unblocked, sequenced correctly, and aligned on shared deliverables.
- Create visibility into program health, surfacing risks and trade\-offs to engineering leads and Games leadership through high\-quality written communication and structured program reviews.
### Enable Studio Analytics and Data Partnerships
- Serve as the TPM interface between Games DSE and game studio partners, internal and external, ensuring studios have the data tools, dashboards, and analytical support they need to understand and improve their games on the Netflix platform.
- Drive requirements gathering and scoping for studio\-facing analytics products, translating developer and producer needs into actionable platform investments.
- Manage the delivery of studio analytics tooling from concept through launch, including integration work with the Games Lifecycle and Developer Ecosystem teams.
- Build and maintain clear communication about data capabilities and roadmap status with game development partners, reducing ambiguity and improving developer satisfaction.
### Own Experimentation and Measurement Infrastructure Programs
- Drive programs that deliver experimentation infrastructure for games, A/B testing frameworks, holdout methodology, experiment configuration tooling, enabling both platform and studio teams to run rigorous, statistically valid experiments.
- Partner with product and engineering leads to translate measurement strategy into concrete technical programs with clear milestones and accountability.
- Track adoption of data and experimentation capabilities across the games portfolio and drive initiatives that close gaps.
### Operate with Technical Depth and Cross\-Functional Influence
- Develop a working understanding of the Games data architecture, telemetry ingestion, data warehouse patterns, event schemas, pipeline orchestration, sufficient to identify technical risks and evaluate trade\-offs without needing full translation from engineers.
- Engage across Games Engineering, Games DSE, Games Studio, and central Netflix Data Platform teams to manage dependencies, align on shared standards, and resolve conflicts between competing priorities.
- Exemplify the Netflix culture by understanding, advocating for, and demonstrating its values in how you lead, communicate, and operate.
What Sets You Apart
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- Direct experience working with or alongside data engineering or analytics engineering teams, pipelines, warehouse, orchestration.
- Familiarity with experimentation platforms and A/B testing methodology.
- Experience supporting game development or game analytics in a studio, platform, or publisher context.
- Comfort working across multiple game platforms, including mobile (Android/iOS), TV, and cloud/web.
- Experience with developer\-facing tools, SDKs, or developer experience programs.
- Ability to leverage GenAI tools to accelerate your work, with a genuine curiosity about how these tools are reshaping technical program management.
Learn More
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- We believe safe spaces where everyone can be their authentic selves are the key to a successful team, so we welcome and embrace all identities, cultures, and backgrounds.
- Our US\-based team is happy to embrace remote work with the expectation that core working hours are PST and with occasional travel to on\-site meetings, typically, but not limited to Los Gatos and/or Los Angeles.
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 $420,000\.00 \- $630,000\.00\. This compensation range will vary based on location.
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.
Job is open for no less than 7 days and will be removed when the position is filled.
Salary Context
This $420K-$630K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 in Demand for This Role
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 $185,000 based on 13,200 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,778. This role's midpoint ($525K) sits 184% above the category median. Disclosed range: $420K to $630K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Netflix AI Hiring
Netflix has 8 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer, Data Scientist, AI Product Manager. Positions span Los Gatos, CA, US, Remote, US, New York, NY, US. Compensation range: $600K - $1066K.
Remote Work Context
Remote AI roles pay a median of $173,300 across 2,012 positions. About 14% of all AI roles offer remote work.
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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. 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 $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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