Locomotion and RL Engineer at Foundation Robotics Labs Inc

San Francisco, California, United States -

Full Time

Start Date

Immediate

Expiry Date

10 Mar, 26

Salary

1000000.0

Posted On

10 Dec, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Reinforcement Learning, Robotics, Control Systems, Python, C++, Deep Learning, Physics Simulation, ROS, CI/CD, Unit Testing, Curriculum Learning, Domain Randomization, Sim2Real Transfer, Energy Efficiency, Robustness, Adaptability

Industry

Robotics Engineering

Description

Why are We Hiring for this Role: 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. What Kind of Person We Are Looking For: 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. Benefits We provide market standard benefits (health, vision, dental, 401k, etc.). Join us for the culture and the mission, not for the benefits. Salary The annual compensation is expected to be between $80,000 - $1,000,000. Exact compensation may vary based on skills, experience, and location.

Responsibilities

The role involves designing, developing, and optimizing reinforcement learning algorithms for humanoid robots, as well as integrating learned policies into real-world platforms. Collaboration with various teams is essential to ensure effective hardware and software integration.