Staff Reinforcement Learning Engineer – Whole Body Control

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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 Staff 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

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