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

- **Company:** Netflix, Inc.
- **Location:** Reading, MA, United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), A/B Testing, Artificial Intelligence, Code Review, Python (Programming Language), Machine Learning, Performance Tuning, Software Engineering, Information Technology, Optimization Algorithms, Machine Learning Operations
- **Published:** September 1, 2026
- **Apply:** https://www.gamesjobsdirect.com/job/netflix-game-studio/ai-research-engineer-6-tl-algo-core-ai-for-member-systems/354190

## About the Role

* 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.

## Description

* 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 Merchandising, 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.


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