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

Netflix•Los Gatos, CA

30d•$600,000 - $1,066,000

About The Position

The Off-Platform and Evidence Personalization org at Netflix includes teams focused on algorithmic notification personalization, asset personalization, and 0-1 GenAI bets. The Messaging team is the most mature and complex, divided into Candidate Generation (scaling message catalog and GenAI creation), Send Decision Optimization (improving targeting), and Emerging Applications (expanding personalization to new areas like Podcasts, FTAB, and Commerce). This role is a tech lead position overseeing vision, strategy, and execution across these teams. The tech lead will own production ML systems at the intersection of product and platform, including GenAI-powered message and asset creation, Commerce, and new content experiences. The role offers cross-functional leadership, high-visibility scope impacting all Netflix members, and the opportunity to work on both building new systems from scratch and optimizing existing ones, with a strong focus on GenAI applications.

Requirements

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

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