Reinforcement Learning Engineer – Whole Body Control

Job description

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 $150,000 and $350,000 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.