Reinforcement Learning Engineer – Whole Body Control

Figure is an AI Robotics company autonomous general-purpose humanoid robots. The goal of the company is to ship humanoid robots with human level intelligence. Its robots are engineered to perform a variety of tasks in the home and commercial markets. We are based in North San Jose, CA and require 5 days/week in-office collaboration. It’s time to build. We are looking for a Reinforcement Learning Engineer to develop, train, deploy, and evaluate advanced reinforcement learning algorithms for whole body control of our humanoid robot. Key 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 Requirements: 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 Bonus Qualifications: Experience with behavior cloning techniques (e.g. distillation) The US base salary range for this full-time position is between $200,000 and $300,000 annually. The pay offered for this position may vary based on several individual factors, including job-related knowledge, skills, and experience. The total compensation package may also include additional components/benefits depending on the specific role. This information will be shared if an employment offer is extended.