Robotics AI Engineer — Real-World RL for Humanoid Robots

Menlo Research; Singapore; On-site; SGD 120,000 - 190,000; Full time

Job summary

Menlo Research seeks a Robotics AI Engineer to design and train RL and imitation policies that run on the Asimov humanoid platform in the real world. You will work with hardware, firmware, and infrastructure teams to close the loop between training and deployment.

You will run experiments on physical robots, build fast data pipelines, and iterate policy improvements with systematic debugging and domain adaptation. This role requires hands-on work with real hardware.

Qualifications

• Strong foundations in reinforcement learning or imitation learning.
• Hands-on experience training policies that run on real systems.
• Proficiency in Python and familiarity with RL/ML frameworks (JAX, PyTorch, IsaacGym/IsaacLab, MuJoCo).
• An empirical, debugging-first mindset, caring about what actually works on hardware.
• Ability to move fast and context-switch between research problems and engineering tasks.

Responsibilities

• 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.

Skills

Reinforcement learning

Imitation learning

Python

RL frameworks

Debugging mindset

Hardware experimentation

Context-switching

Tools

JAX

PyTorch

IsaacGym/IsaacLab

MuJoCo

Job description

Menlo Research seeks a Robotics AI Engineer to design and train RL and imitation policies that run on the Asimov humanoid platform in the real world. You will work with hardware, firmware, and infrastructure teams to close the loop between training and deployment.

You will run experiments on physical robots, build fast data pipelines, and iterate policy improvements with systematic debugging and domain adaptation. This role requires hands-on work with real hardware.