Reinforcement Learning Engineer – Whole Body Control

Figureai; San Jose (CA); On-site; USD 200,000 - 350,000; Full time

Job summary

Figureai in San Jose, CA is seeking a Reinforcement Learning Engineer to develop and train algorithms for the control of humanoid robots. You will work on improving performance and ensuring robust control systems through innovative RL techniques.

The ideal candidate should have experience in dynamics, control, and robotics, along with the ability to mentor others. A competitive salary between $200,000 and $350,000 annually is offered for this role.

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 and reward shaping.

Responsibilities

• Develop, train, and deploy reinforcement learning algorithms for whole body control.
• Determine observations, actions, and model types for maximum performance.
• Identify and close important sim-to-real gaps.
• Define, test, and evaluate performance metrics for learned policies.
• Harden the control stack for robustness.

Skills

Dynamic systems control

Reinforcement Learning algorithms

Hyperparameter tuning

Mentoring

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 $200,000 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.