Reinforcement Learning Researcher

What You'll Do: Drive offline reinforcement learning research from start to finish, including choosing which questions to pursue and communicating the outcomes. Raise the performance of learned control policies on internal evaluation suites and build on earlier policy work. Train models on large, uneven-quality collections of data where feedback is scarce, applying several value estimation approaches. Investigate model-based methods and learned environment models for continuous control. Examine what keeps reinforcement learning training stable as data, compute and model scale increase. Design rigorous experiments with dependable baselines and watch findings get tested on physical robots.

What We're Looking For: Background in reinforcement learning research, in a research or applied scientist capacity, connected to generative visual models. A record of papers accepted at leading machine learning or robotics venues. Depth in core and offline reinforcement learning topics, such as value estimation, goal-directed learning, policy design and training stability at scale. Familiarity with model-based methods for continuous control or with multimodal value estimation. An advanced degree (PhD or master's) in machine learning, computer science or an adjacent area, centered on reinforcement learning. Ability to work on-site six days a week.

Bonus Points: Prior exposure to visual generative models applied to robotics. Time spent doing research at a top-tier AI organization. Contributions to publicly shared reinforcement learning codebases. Experience with large-scale simulation, sim-to-real transfer or robotics, including JAX.

Benefits: Health, dental and vision coverage Location and Work ModelSan Francisco, CA, United StatesOn-site, 6 days per week in office Full-time Compensation, As The Posting Describes ItEquity