RL Locomotion Researcher for Real-World Humanoids

Menlo; United States; On-site; USD 120,000 - 180,000; Full time

Job summary

Menlo Research is seeking a Researcher in Locomotion to push Asimov from walking to running, recovering, and navigating real-world terrains. You will own policies that make motion robust on hardware, bridging simulation to real robots with zero-shot strategies.

You will collaborate with hardware, controls, and manipulation researchers to refine balance, gait, and recovery. Expect to contribute to open-source project work and publish learnings with the team.

Qualifications

• Experience with reinforcement learning for continuous control, legged locomotion, or whole-body control.
• Track record of getting learned policies onto real robots, not only into papers or simulators.
• Strong command of physics simulators (MuJoCo, Isaac) and domain randomization.
• Fluency in Python and RL tooling; comfortable with a ROS2-based control stack.

Responsibilities

• Design, train, and ship reinforcement learning policies for bipedal and whole-body locomotion on Asimov.
• Own the sim2real pipeline end to end, building on zero-shot sim2real work so hardware runs on first tries.
• Push balance, gait, and recovery beyond demos to handle pushes and uneven terrain.
• Build and refine reward design, domain randomization, and training environments in MuJoCo.

Skills

Reinforcement learning

Continuous control

Legged locomotion

Whole-body control

Python

ROS2

Policy deployment

Sim2real

Tools

MuJoCo

Isaac

ROS2-based stack

Job description

Menlo Research is seeking a Researcher in Locomotion to push Asimov from walking to running, recovering, and navigating real-world terrains. You will own policies that make motion robust on hardware, bridging simulation to real robots with zero-shot strategies.

You will collaborate with hardware, controls, and manipulation researchers to refine balance, gait, and recovery. Expect to contribute to open-source project work and publish learnings with the team.