Interested in this AI/ML Engineer role at Netflix?
Apply Now →Skills & Technologies
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 Team
The Infrastructure and GenAI Technical Program Management organization sits within Engineering Operations (Eng Ops), which helps Netflix's engineering teams move faster and more reliably at scale. Eng Ops serves as the strategic and operational arm of the broader Infrastructure and Security organization, spanning functional disciplines including Platform Modernization, Developer Education, Support, SRE, and Technical Program Management.
Infrastructure and GenAI TPM serves as the connective tissue across engineering, turning broad, complex technical portfolios into coherent, well-governed programs. TPMs act as strategic partners to Engineering and Product Management, driving these technical programs and influencing leaders across the organization. Often the first dedicated team members in this capacity, they establish the operating rhythms that keep execution on track, manage cross-team dependencies and partnerships, surface risks before they become a fire drill, and translate strategy into plans that teams can actually execute against.
A top priority for the org is GenAI, enabling the broader organization to build GenAI platforms and driving the shift to becoming GenAI-native.
We are hiring 3 distinct L6 Technical Program Managers to lead critical initiatives within Netflix’s Infrastructure and Security Organization. Each role will drive high-impact, cross-functional programs supporting the different areas below:
AI Platform
This TPM will serve as connective tissue across Netflix's AI Platform, its customers, and the infrastructure teams it relies on. You will partner with AI/ML research and engineering teams, infrastructure engineers, PMs, leaders, and fellow TPMs to transform a broad, complex technical portfolio into a coherent, opinionated AI infrastructure platform. You will drive adoption, scalability, and modernization across data and model infrastructure, training and offline AI infrastructure, and inference at scale, including model serving, routing, and experimentation. As a strategic partner embedded early in a growing engineering organization, you will shape platform direction and operating rhythms, establish paved paths, improve lifecycle visibility, and build the reliability, security, and efficiency required to deploy and operate AI safely and effectively at Netflix's scale.
Developer Platform - GenAI
This TPM will focus on translating the Workforce AI Program’s strategy into measurable gains in capacity, delivery speed, quality, and cost efficiency by driving its highest-priority initiatives. You will act as connective tissue across technical and enterprise teams, leaders, and fellow TPMs. This role will align stakeholders, sequence work, manage complex dependencies and risks, and coordinate delivery across AI experiences, platforms, infrastructure, third-party products, and adoption programs. Success requires navigating evolving technologies, ambiguity, and distributed ownership while ensuring AI solutions scale responsibly, securely, and cost-effectively, earn user trust, and consistently connect technical investments to meaningful business outcomes.
Data Platform
This TPM will partner with the Data Platform and Online Data Stores & Movement Platform (ODSMP) engineering team - one of the most foundational and business-critical teams at Netflix. As Netflix builds toward an agentic infrastructure powered by GenAI, demand for real-time, low-latency data patterns - including feature stores, RAG pipelines, and AI-facing retrieval is accelerating, and ODSMP sits at the center of enabling the infrastructure that AI-native products and systems depend on. The TPM in this role will be a strategic partner to the engineering team and will act as the first team member as they navigate that growth, turning a broad, complex technical portfolio into a coherent, managed agentic platform with opinionated paved paths, while strengthening reliability, security, and cost governance at scale. The TPM will lead the team's core operating rhythms, manage the health of our partnerships, and drive technical cross-team programs that move the platform forward.
Responsibilities:
A successful candidate will have experience driving large-scale technical programs across infrastructure and will be energized by the opportunity to help shape how AI and agentic systems are built and operated at scale. They will be comfortable operating in technical ambiguity, applying systems thinking to complex technical environments, influencing technical and strategic direction and translating emerging AI platform needs into executable programs that meaningfully impact thousands of engineers across Netflix.
- Partner closely with leaders, engineers, PMs, and practitioners to identify, influence and frame net-new problem spaces before they are formally defined, then structure them into technical execution plans with well-defined milestones, ownership, and success criteria.
- Personally drive execution rigor across complex, multi-team programs, tracking progress, managing dependencies, unblocking issues, and owning delivery outcomes end to end while implementing minimal process.
- Influence org-level investment decisions, resource planning, and technical roadmaps in partnership with engineering and product leaders.
- Communicate clearly and consistently with technical leadership and stakeholders on progress, risks, and decisions.
- Use insights from program execution to influence platform evolution, architectural direction, and operational practices over time.
Requirements:
- 10+ years of experience leading large-scale technical programs in infrastructure, internal platforms, or distributed systems, including significant time leading programs with org-level scope and executive stakeholders.
- Demonstrated understanding of the GenAI tech stack, along with experience driving complex GenAI programs across the org.
- Proven experience creating partnerships with cross-functional teams, driving large-scale technical strategies, debating technical approaches, and building long-term scalable solutions with engineers.
- Proven ability to structure ambiguous, undefined technical problem spaces into executable programs, not just manage against a roadmap someone else defined.
- Proven ability to identify gaps in solutions and weigh in on product vs. technology trade-offs.
- Excellent written and verbal communication skills, with the ability to articulate technical constraints, trade-offs, and business impact to diverse audiences.
- Understanding of challenges in high-scale distributed systems, architectures, and data layers.
What sets you apart?
You have operated in 0* 1 and evolving problem spaces, quickly bringing order and execution discipline.
- You're equally comfortable setting the strategic direction and doing the hands-on work to move it forward, e.g. building the program plan, writing the technical alignment doc, pulling the data, and rallying and running the working group.
- You operate as a strategic thought partner to VPs and Directors, not just a program tracker.
- You enjoy orchestrating programs with many moving parts - people, needs, and technologies.
- You have a deep understanding and point of view within your technical domain(s).
- You have a healthy blend of technical knowledge with a customer-focused mindset and empathy.
- You are a strong systems thinker with the ability to connect infrastructure capabilities to business outcomes.
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: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($525K) sits 148% above the category median. Disclosed range: $420K to $630K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Netflix AI Hiring
Netflix has 5 open AI roles right now. They're hiring across AI/ML Engineer, Research Scientist. Positions span Remote, US, Los Gatos, CA, New York, NY. Compensation range: $630K - $750K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.