# Reinforcement Learning Researcher > Raydar · San Francisco, United States · Full-time · Posted 2026-10-15 **Salary:** USD 200,000–350,000 **Workplace:** on_site ## Description 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 ## Apply [Apply at Raydar](https://apply.workable.com/raydar/j/60183DEE15/apply) --- Powered by [Workable](https://www.workable.com)