Research Engineer 4/5 - Member Lifecycle and Monetization

$466K - $750K Remote Mid Level Research Engineer

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Skills & Technologies

Python

About This Role

AI job market dashboard showing open roles by category

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 Member Lifecycle and Monetization Data Science \& Engineering team plays a critical role for Netflix in driving and accelerating sustainable growth of members and revenue globally, by leveraging data, experimentation \& machine learning to develop compelling and persuasive conversion and monetization experiences post\-signup to optimize revenue per member. Machine Learning in these areas is a relatively greenfield area, and comes with the potential for 0\-1 applications that can drive millions of dollars of impact at Netflix’s scale.

We are looking for a research engineer to join the team to contribute to operating, as well as innovating on growth and commerce algorithms in production, validating through running offline experiments, and building online A/B tests to run in production systems. You’ll partner with other ML engineers, scientists and product managers on cross\-functional ML initiatives.

To excel in this role, you should have experience with large\-scale applications involving machine learning, a good sense of software engineering principles and design, possess strong communication skills, and the ability to work well in large cross\-functional teams.

In this role, you will:

  • Design, implement and operate high impact machine learning models
  • Partner closely with cross\-functional teams, including researchers, engineers, data scientists, and product managers, to identify high value applications of machine learning, translating business intuition into data\-driven solutions
  • Work closely with scientists and engineers to create scalable, production\-ready ML solutions, taking algorithms from initial concept through to deployment in Netflix's large\-scale, real\-time systems.
  • Contribute to the development of better infrastructure for developing and deploying ML models
  • Advocate for and apply best practices when it comes to availability, scalability, operational excellence, and cost management

What you’ll bring:

  • A degree in Computer Science or a related field
  • 4\+ years of full time engineering experience
  • Curious, self\-motivated, and excited about solving open\-ended challenges at Netflix.
  • Excellent software design and development skills in multi\-language settings with Scala, Java, and Python and software engineering best practices (e.g. version control, testing, code review, etc.)
  • Exceptional communication skills, able to explain complex technical concepts clearly to cross\-functional partners
  • Broad understanding of core machine learning concepts and their application in large\-scale, real\-world machine\-learning systems
  • Familiarity end\-to\-end machine learning pipelines (e.g. training or production deployment) and common challenges like explainability.

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 $466,000\.00 \- $750,000\.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 $466K-$750K range is above the 75th percentile for Research Engineer roles in our dataset (median: $207K across 63 roles with salary data).

View full Research Engineer salary data →

Role Details

Company Netflix
Title Research Engineer 4/5 - Member Lifecycle and Monetization
Location Remote, US
Experience Mid Level
Salary $466K - $750K
Remote Yes

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 4,317 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

Python (52% of roles)

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 $272,100 based on 227 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($608K) sits 123% above the category median. Disclosed range: $466K to $750K.

Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and AI Engineering Manager ($244,000). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.

Netflix AI Hiring

Netflix has 8 open AI roles right now. They're hiring across Research Engineer, Research Scientist, AI/ML Engineer, AI Product Manager. Positions span Remote, US, Los Gatos, CA, US. Compensation range: $560K - $1195K.

Remote Work Context

Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.

The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.

Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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

Based on 227 roles with disclosed compensation, the median salary for Research Engineer positions is $272,100. Actual compensation varies by seniority, location, and company stage.
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.
About 15% of the 4,317 AI roles we track offer remote work. Remote availability varies by company and seniority level, with senior and leadership roles more likely to offer location flexibility.
Netflix is among the companies actively hiring for AI and ML talent. Check our company profiles for detailed breakdowns of open roles, salary ranges, and hiring trends.
Common next steps from Research Engineer positions include Senior Research Engineer, Research Scientist, ML Architect. Progression depends on whether you lean toward technical depth, people management, or product strategy.

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