Reinforcement Learning Engineer, Whole Body Control
Figure· Controls
San Jose, CA$150k - $350kfull-timePosted 6mo ago
About the role
Reinforcement Learning Engineer to develop, train, deploy, and evaluate advanced reinforcement learning algorithms for whole body control of humanoid robots.
Responsibilities
- Develop, train, and deploy reinforcement learning algorithms for whole body control.
- Determine the observations, actions, and model types that unlock maximum performance.
- Identify and close the most important sim-to-real gaps.
- Define, test, and evaluate performance metrics for learned policies.
- Harden the control stack to ensure rock solid robustness.
Qualifications
- Strong background in dynamics and control, ideally of legged robots.
- Experience with reinforcement learning algorithms for robotics: PPO, SAC, etc.
- Experience tuning hyperparameters and cost functions for these RL algorithms.
- Familiarity with common RL techniques such as: domain randomization, curriculum learning, reward shaping, etc.
- Capable of leading complex controls projects and mentoring junior engineers.
Skills
Required:Dynamics and controlReinforcement learningHyperparameter tuningDomain randomization, curriculum learning, reward shaping
Last updated Oct 7, 2026. Interested? Apply on the company site.
About Figure
The future of home help is here
Figure is the first-of-its-kind AI robotics company bringing a general purpose humanoid to life.
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