Senior Reinforcement Learning Engineer, Humanoid Locomotion and Manipulation
Human-centered robotics company building AI-powered humanoid robots for manufacturing, logistics and beyond, Bay Area
| Employer | Human-centered robotics company building AI-powered humanoid robots for manufacturing, logistics and beyond, Bay Area (not named) |
|---|---|
| Location | Sunnyvale, CA (Bay Area) · On-site |
| Type & level | Full-time · Senior |
| Pay | USD 230k–260k base Source: pay range stated in the source posting |
| Visa path | O-1A fit (senior candidates) |
| Role family | Robot Manipulation & Dexterous Control Researcher |
About the role
This hands-on role applies reinforcement learning to dynamic locomotion and manipulation on physical humanoid hardware. The engineer takes policies from simulation prototypes to robust transfer on the robot and also mentors junior engineers. The goal is state-of-the-art performance on real hardware rather than simulation-only results.
What you'll do
- Implement and deploy modern RL algorithms for dynamic locomotion and manipulation tasks on physical robots.
- Run the full cycle from simulation prototyping to sim-to-real transfer and fine-tuning of policies.
- Optimize and scale the training pipeline, including high-throughput simulation and distributed training.
- Build motion retargeting pipelines that turn human demonstration data such as mocap and teleoperation into reference trajectories.
- Work with robotics and hardware teams to diagnose system-level issues and enable more complex learned behaviors.
- Mentor junior engineers through code reviews and technical guidance, and present hardware results to guide direction.
Requirements
- 5+ years of hands-on experience with RL frameworks such as PyTorch or JAX and physics simulators such as MuJoCo or IsaacGym.
- Strong Python for prototyping and training, and strong C++ for deployable code.
- Experience building or using large-scale distributed training pipelines.
- Solid theory of modern RL, including imitation learning, model-based RL and sim-to-real transfer.
- Understanding of robot dynamics and control theory to guide learning-based approaches.
- PhD or MS in Computer Science, Robotics or a related field, with 2+ years of industry experience strongly preferred.
Nice to have
- Track record deploying learned policies on physical robots, especially legged robots or manipulators.
- Experience mentoring or technically guiding other engineers.
- Publications at venues such as CoRL, RSS or ICRA.
Pay & benefits
USD 230k–260k base. The posting does not list benefits.
Pay source: pay range stated in the source posting. Market data, not an offer from O1DMatch.
Visa path
Senior robot-learning engineers often build an O-1A case from peer-reviewed publications, citations, conference service, original contributions to learned control 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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