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Netflix

Research Engineer 6 - TL, Messaging Algorithms

Los Gatos, United States

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Job details

Apply by: May 6, 2027
Experience: 10+ years of experience applying machine learning in an industry setting, with a track record of delivering impactful production systems
Posting language: English
Posted: Nov 7, 2026

Original job description

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 Notifications Personalization team owns the algorithmic targeting, ranking, and pacing of notifications to Netflix members. We build the ML systems that decide which messages to send, to whom, when, how often, and via which channel. The team partners closely with the broader ML organization (ML Platform, Recommendations, Foundation Models) and the broader messaging ecosystem (Messaging Engineering, Product, CRM, Live, Games).

The team is organized 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 within the team. You will co-own vision and strategy, as well as drive execution for key technical initiatives across 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 touches every Netflix member. 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 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

10+ 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
Strong software engineering skills in Python, plus experience with Scala or Java
Strong system design skills, keeping operational simplicity in mind
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 .

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.

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More about this job

Skills

Research
ML
Leadership
Merchandising
Machine learning
Computer science
Statistics
Python
Scala
Oracle Java
Mental health

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