Robotics

ML, Reinforcement Learning

Redwood City, CAFull TimePosted 23 days ago

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Required skills

reinforcement learning
locomotion policies
learned behaviors
whole-body control
sim-to-real transfer
real-world robot experience
physical systems
policy optimization
value methods
model-based RL

Nice to have

legged locomotion
humanoid
bipedal
contact-rich tasks
force-aware policies
imitation-learned policies

Role summary

Seeking a Machine Learning researcher specializing in Reinforcement Learning (RL) to own RL across the company's stack, from locomotion policies to learned behaviors. This role involves setting technical direction, advancing the state of the art in RL for embodied systems, and shipping results on real robots. Responsibilities include applying RL to improve learned behaviors, working on whole-body control, learning from fleet data using offline and hybrid RL, and owning reward design and exploration. Requires a PhD or equivalent research experience in RL or robotics, depth in RL fundamentals, and strong engineering skills for scaling RL. Experience training policies on physical systems and handling partial observability is crucial.

About the Role

You will own reinforcement learning across our stack, from locomotion policies to RL applied on top of learned behaviors. The research problems here are open ones: whole-body control under contact, sim-to-real transfer that holds up in environments we have never seen, and learning from a growing body of real-world robot experience.

This is a role for someone who wants their research to run on hardware in the field rather than on a benchmark, and who is early enough in a team's life to set its technical direction.

What You'll Do

Own RL across our stack, from locomotion to learned behaviors, and set the technical direction for how we apply it.
Track where the field is going, and make calls about which directions are worth betting on and which are noise.
Advance the state of the art in RL for embodied systems, and ship the results on real robots.
Apply RL to improve learned behaviors beyond what demonstrations alone can reach, including fine-tuning imitation-learned and pretrained policies.
Work on whole-body control, where the robot maintains balance while applying and reacting to external forces.
Learn from logged fleet data, applying offline and hybrid RL to experience collected from robots in the field.
Own reward design, curriculum, and exploration, and the diagnosis of why a policy is failing.
Drive sim-to-real transfer, working with Simulation on fidelity and randomization strategy.

What We're Looking For

PhD in reinforcement learning, robotics, or a closely related field from a group doing leading work, or equivalent research experience at a frontier lab.
Depth in RL fundamentals: policy optimization, value methods, model-based and offline RL, and the practical failure modes of each.
Research contributions that others build on: publications at leading venues, or widely used open source work. You have moved the state of the art, not only kept up with it.
Experience training policies that ran on physical systems, and the judgment that comes from watching them fail.
Strong engineering skills. RL at scale is an infrastructure problem as much as an algorithms problem.
Experience with partial observability and environment variability, where the policy has to work in conditions it was not trained on.

Bonus Points

Legged locomotion, particularly humanoid or bipedal.
Whole-body control, contact-rich tasks, or force-aware policies.
RL fine-tuning on top of imitation-learned or pretrained policies.
Experience at a leading AI or robotics lab.

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