Senior AI Researcher, Robot Learning (Manipulation and Locomotion RL)
Humanoid home-robot company, Bay Area, with a motion team covering perception and whole-body control
| Employer | Humanoid home-robot company, Bay Area, with a motion team covering perception and whole-body control (not named) |
|---|---|
| Location | San Carlos, CA (Bay Area) · On-site |
| Type & level | Full-time · Senior |
| Pay | USD 200k–300k + equity Source: pay range stated in the source posting |
| Visa path | O-1A fit |
| Role family | Robot Manipulation & Dexterous Control Researcher |
About the role
The motion team builds the perception and whole-body control that let a home humanoid move around people safely. This researcher owns reinforcement learning from algorithm development to production deployment for manipulation and locomotion. The range of tasks the robot can do reliably depends directly on the quality of the policies this team ships.
What you'll do
- Train and deploy RL policies for manipulation and locomotion that work in real home environments, judged by field task success.
- Advance sim-to-real transfer methods to narrow the gap between simulated and real behavior.
- Build training and evaluation infrastructure with standard benchmarks and automated regression detection.
- Link training metrics to field performance so the team can iterate faster.
- Partner with hardware, controls, data and QA teams to ship learned skills to customer sites.
- Own the handoff from research to deployment.
Requirements
- Strong Python and/or C++ with experience in large codebases and build tools such as Bazel.
- Proficiency with PyTorch for RL policy training.
- Hands-on use of simulation platforms such as Isaac Sim or MuJoCo for large-scale training.
- Demonstrated experience training RL policies for manipulation or locomotion, including closing the sim-to-real gap on hardware.
Nice to have
- Model-based RL or world-model-guided policy learning.
- Imitation learning or learning from demonstration, such as behavior cloning, GAIL or IQL.
- Experience deploying RL policies on production robots, including monitoring and failure analysis.
- Background in legged locomotion, dexterous manipulation or contact-rich control.
Pay & benefits
USD 200k–300k + equity. The posting lists medical, dental and vision coverage, paid time off and parental leave, a 401(k) with company match, FSA/HSA options, commuter benefits, disability and life insurance, and an assistance program, plus equity.
Pay source: pay range stated in the source posting. Market data, not an offer from O1DMatch.
Visa path
Robot learning researchers commonly build an O-1A case from peer-reviewed publications, citations, reviewing and conference service, original contributions to sim-to-real and RL methods, and a high salary. The posting states no citizenship restriction. An employer like this, or a U.S. agent, can file the petition if the evidence meets the standard; approval is decided by the government and is not assured.
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