Reinforcement Learning Engineer, Helix

Job Description:

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 San Jose, 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.