AI Research Engineer 6 - TL, Algo Core - AI for Member Systems

Netflix•Remote, OR

64d•$600,000 - $1,066,000

About The Position

Algo Core is a horizontal team within AI for Member Systems (AIMS) that owns reward modeling, utility estimation, and multi-objective optimization as shared capabilities used across Netflix’s recommendation, personalization, and promotion systems. We’re looking for a Research Engineer to serve as technical lead for this surface area — driving the roadmap for how Netflix combines member value and business value into a single, tunable framework (using auction-based and constrained-optimization techniques) for deciding what gets promoted where across the member experience.

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.
Master’s or PhD in a computational field such as computer science, statistics, math, operations research, or physics.
Deep expertise in ML and optimization algorithms and frameworks, with hands-on experience training, tuning, and deploying models in production.
Experience with reward modeling, utility estimation, or constrained-optimization and auction-based allocation 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 reward modeling, utility estimation, and multi-objective optimization — including auction-based and constrained-optimization approaches to allocating promotional real estate.
Drive cross-functional partnerships with Merchandining, Ads, MECS, Product, and Data Science & Engineering to align AI/ML capabilities with business priorities.
Partner with the Personalization Foundations team to integrate and leverage the utility layer, reward signals, and multi-objective optimization across all member-facing AI models.
Design and run rigorous offline experiments and A/B tests to validate the impact of new reward, utility, and multi-objective optimization systems on key business and member-experience metrics.
Contribute to the team’s technical culture through mentorship, code review, and raising the bar on engineering practices.