Interested in this Research 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.
About the Team
------------------
The Off\-Platform and Evidence Personalization org includes 3 teams: algorithmic notification personalization, asset personalization, and 0\-1 GenAI bets. Notifications builds the ML systems that decide which messages to send, to whom, when, how often, and via which channel, while asset personalization leads multimodal generation, recommendation, and tagging to help members make informed viewing decisions. The teams partner closely with the broader ML organization (ML Platform, Recommendations, Foundation Models) and a diverse cross\-functional organization (Engineering, Product, Data Science, CRM, Live, Games).
Messaging is the most mature and complex of the teams. It is further divided into three areas:
- Candidate Generation: Scaling the message catalog via creator tooling integrations and GenAI message creation
- Send Decision Optimization: Improving targeting decisions across the notification system
- Emerging Applications: Expanding message personalization into new business areas and member states including Podcasts, FTAB, and Commerce applications.
About the Role
------------------
This position is a tech lead role across all teams. The tech lead will own the vision and strategy for the area, as well as drive execution for key initiatives within individual teams. You will own production ML systems at the intersection of product and platform, spanning GenAI\-powered message and asset creation, Commerce, and new content experiences.
What makes this role unique:
- Cross\-functional leadership. You'll drive partnerships with Merchandising, Product Management, Data Science \& Engineering to develop scalable, well\-integrated designs.
- High\-leverage, high\-visibility scope. Messaging and Assets touch every Netflix member, and 0\-1 bets have the potential to reshape who interacts with Netflix and how. The systems you build will directly drive engagement, retention, and revenue across some of Netflix's fastest\-growing business areas.
- GenAI meets product. Personalized message and asset creation is a natural application of multimodal AI. You'll be at the forefront of bringing GenAI capabilities into a production messaging system at global scale.
- 0 1 and optimization. The role spans building entirely new systems from scratch (Rejoin message personalization, GenAI message creation) and applying advanced methods to further optimize existing levers.
Responsibilities
--------------------
- Drive the team’s technical vision and roadmap for candidate generation and emerging applications
- Drive cross\-functional partnerships with Merchandising, Product, Engineering, and Data Science \& Engineering to align ML capabilities with business priorities
- Design, build, and ship production ML systems that scale across Netflix's ecosystem
- Partner with the AIMS AI Foundations team to integrate and leverage foundation model capabilities for member\-facing use cases
- Design and run rigorous offline experiments and A/B tests to validate the impact of new systems on key business metrics
- Contribute to the team's technical culture through mentorship, code review, and raising the bar on engineering practices
What We're Looking For
--------------------------
- 6\+ years of experience applying machine learning in an industry setting, with a track record of delivering impactful production systems
- Experience driving successful partnerships with both technical and nontechnical stakeholders
- Masters or PhD in a computational field such as physics, computer science, statistics, or math
- Deep expertise in ML algorithms and frameworks, with hands\-on experience training, tuning, and deploying models in production
- Experience with personalization, recommendations, or search algorithms
- Experience with GenAI, LLMs, or multimodal AI in production systems
- Strong software engineering skills in Python, plus experience with Scala or Java
- Strong 80/20 mindset: ability to scope the right problem, ship pragmatically, and maintain rigorous standards without over\-engineering
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\. 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 $600K-$1066K range is above the 75th percentile for Research Engineer roles in our dataset (median: $188K across 44 roles with salary data).
View full Research Engineer salary data →Role Details
About This Role
Research Engineers bridge the gap between research and production. They implement papers, build experiment infrastructure, optimize training pipelines, and make research prototypes production-ready. They're the engineers who make research work at scale.
The role sits at a unique intersection. You need to understand the math well enough to implement novel architectures correctly, and you need the engineering chops to make them run efficiently on distributed systems. When a research scientist has a breakthrough idea, you're the person who turns it from a notebook prototype into a training pipeline that runs on 256 GPUs.
Across the 3,708 AI roles we're tracking, Research Engineer positions make up 2% of the market. At Netflix, this role fits into their broader AI and engineering organization.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
What the Work Looks Like
A typical week involves: implementing a new attention mechanism from a recent paper, profiling and optimizing a training pipeline that's bottlenecked on data loading, building evaluation infrastructure for a new benchmark, debugging distributed training issues across a GPU cluster, and pair-programming with a research scientist on their latest experiment. The work is deeply technical.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
Skills Required
Strong software engineering fundamentals plus ML knowledge. Python, C++, and CUDA experience are common requirements. You'll need to read papers and turn ideas into working code. Distributed systems experience (especially distributed training) is highly valued. Performance optimization skills separate great candidates from good ones.
Experience with large-scale training infrastructure (FSDP, DeepSpeed, Megatron), GPU programming (CUDA, Triton), and the internals of ML frameworks (PyTorch internals, custom autograd functions) is what makes candidates stand out. The best research engineers can debug issues that span the full stack from GPU memory management to numerical precision to algorithmic correctness.
Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
Compensation Benchmarks
Research Engineer roles pay a median of $280,000 based on 147 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($833K) sits 198% above the category median. Disclosed range: $600K to $1066K.
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 AI Architect ($254,798). 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.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
Career Path
Common paths into Research Engineer roles include Software Engineer, ML Engineer, Research Intern.
From here, career progression typically leads toward Senior Research Engineer, Research Scientist, ML Architect.
This is one of the best entry points into AI research without a PhD. Build a strong engineering portfolio with ML projects, contribute to open-source ML frameworks, and demonstrate that you can implement complex ideas correctly and efficiently. The transition to Research Scientist is possible with published first-author work, which some research engineer roles support.
What to Expect in Interviews
Technical screens test both engineering skill and research understanding. Expect coding rounds with performance-critical implementations (GPU optimization, efficient data loading). Be prepared to discuss papers relevant to the team's research area and explain how you'd implement key ideas. System design questions focus on training infrastructure: distributed training, experiment tracking, and compute resource management.
When evaluating opportunities: Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
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).
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.