Reinforcement Learning Researcher

About the company Our client is a technology company. The role Raydar is recruiting for this role on behalf of our client. Lead a research agenda in offline reinforcement learning, taking ideas from initial question through to shared findings. The position prizes scientific depth and judgment over engineering breadth, and pairs you with a more established colleague while leaving space to chart your own path. 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. Requirements 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 Compensation and benefits - Base salary: USD 200,000 to 350,000 per year - Equity - Health, dental and vision coverage Location and work model - San Francisco, CA, United States - On-site, 6 days per week in office - Full-time