Robotics AI Engineer

The role

You will design and train RL and imitation learning policies for locomotion, manipulation, or whole-body control. You will work on physical robots to close the sim-to-real gap and collaborate with hardware, firmware, and infrastructure teams.

What you will do

• Design and train RL and imitation learning policies for locomotion, manipulation, or whole-body control
• Run experiments on physical hardware and close the sim-to-real gap through systematic debugging and domain adaptation
• Build and maintain simulation environments and data pipelines that support fast policy iteration
• Instrument robot deployments and analyze failure modes to feed improvements back into training
• Collaborate with hardware and firmware engineers to understand physical constraints and improve policy robustness

Required qualifications

• Strong foundations in reinforcement learning or imitation learning
• Hands-on experience training policies that run on real systems
• Comfort working directly with robots, not just simulators
• Proficiency in Python
• Familiarity with standard RL/ML frameworks (JAX, PyTorch, IsaacGym/IsaacLab, MuJoCo, or similar)
• An empirical, debugging-first mindset

Nice to have

• Prior work on humanoid or legged robot platforms
• Experience with sim-to-real transfer techniques (domain randomization, system identification, noise injection)
• Contributions to open-source robotics projects
• Background in control theory, trajectory optimization, or dynamics

About the company

Menlo Research is an Applied R&D lab building Asimov, an open-source humanoid robot platform, and the full software stack that powers it.