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