About this role
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