Locomotion and RL Engineer

Foundation•San Francisco, CA

13d•$80,000 - $1,000,000

About The Position

Design, develop, and optimize reinforcement learning algorithms for real-time control and locomotion of humanoid robots. Integrate learned policies into real-world robot platforms with hardware-in-the-loop validation. Collaborate with mechanical, perception, and embedded systems teams to ensure tight integration between hardware and software. Apply advanced techniques such as curriculum learning, domain randomization, and sim2real transfer to improve policy generalization. Analyze and optimize control performance with a focus on robustness, energy efficiency, and adaptability. Contribute to the continuous development of our in-house RL training pipelines and tooling. 2+ years of experience in reinforcement learning applied to robotics or control systems. Strong understanding of classical and modern control theory, locomotion dynamics, and optimization techniques. Hands-on experience with physics simulation environments (e.g., MuJoCo, Isaac Gym, PyBullet). Proficiency in Python and/or C++ for algorithm development and deployment. Experience with deep learning frameworks (e.g., PyTorch, TensorFlow). Familiarity with ROS/ROS2 and real-time robotic systems.

Requirements

2+ years of experience in reinforcement learning applied to robotics or control systems.
Strong understanding of classical and modern control theory, locomotion dynamics, and optimization techniques.
Hands-on experience with physics simulation environments (e.g., MuJoCo, Isaac Gym, PyBullet).
Proficiency in Python and/or C++ for algorithm development and deployment.
Experience with deep learning frameworks (e.g., PyTorch, TensorFlow).
Familiarity with ROS/ROS2 and real-time robotic systems.

Nice To Haves

2+ years of experience in machine learning (NNs, LVMs) and reinforcement learning applied to robotics or similar realtime environments.
Hands-on experience with physics simulation environments (e.g., MuJoCo, Isaac Lab).
Proficiency in Python and C++ for algorithm development and deployment.
Experience with deep learning frameworks (e.g., PyTorch, JAX, TensorFlow).
Familiarity with ROS/ROS2 and real-time robotic systems.
strong software development experience, including CI/CD, unit testing, etc.
Experience deploying RL algorithms on physical robots.
Experience with high-performance computing for distributed training.
Contributions to open-source RL or robotics projects.
M.Sc. or Ph.D. in Robotics, Computer Science, Mechanical Engineering, or a related field.

Responsibilities

Design, develop, and optimize reinforcement learning algorithms for real-time control and locomotion of humanoid robots.
Integrate learned policies into real-world robot platforms with hardware-in-the-loop validation.
Collaborate with mechanical, perception, and embedded systems teams to ensure tight integration between hardware and software.
Apply advanced techniques such as curriculum learning, domain randomization, and sim2real transfer to improve policy generalization.
Analyze and optimize control performance with a focus on robustness, energy efficiency, and adaptability.
Contribute to the continuous development of our in-house RL training pipelines and tooling.