Reinforcement Learning Engineer, Helix

Figure is an AI Robotics company developing a general purpose humanoid. Our Humanoid is designed for corporate tasks targeting labor shortages and jobs that are undesirable or unsafe. We are based in Sunnyvale, CA and require 5 days/week in-office collaboration. It’s time to build.

We are looking for a Reinforcement Learning Engineer. You will own the development, training, and deployment of new reinforcement learning algorithms for our humanoid robot as well as building infrastructure to support training policies at a large scale.

Responsibilities:

• Develop, train, and deploy reinforcement learning algorithms for locomotion and manipulation tasks
• Build simulation infrastructure to support the training of locomotion and manipulation policies for a general purpose humanoid robot at a large scale
• Collaborate with the controls team to integrate policies into the existing control stack
• Define, test, and evaluate performance metrics for learned policies

Requirements:

• Confident writing production quality code in PyTorch
• Familiar with online and offline reinforcement learning algorithms: 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.
• Familiarity with general ML evaluation tools such as TensorBoard, Weights&Biases, etc.

Bonus Qualifications:

• Experience transferring policies learned in simulation to robot hardware
• Experience training locomotion policies for quadrupedal or bipedal robots

The US base salary range for this full-time position is between $150,000 - $400,000 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.