Research Engineer 6 - TL, Off-Platform and Evidence Personalization

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 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.

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.

• 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.

• 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

• 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.

Job is open for no less than 7 days and will be removed when the position is filled.