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