Menlo Research
Robotics AI Engineer
Singapore +1 · Hybrid · Full-time · Posted Jun 17, 2026
- Location
- Singapore
Palo Alto
About the job
Trains and deploys learning policies on Menlo’s Asimov humanoid robot, iterating between simulation and physical hardware.
What you'll do
- Design and train reinforcement and imitation learning policies for locomotion, manipulation, or whole-body control.
- Run hardware experiments, debug sim-to-real issues, and analyze deployment failures to improve training.
- Build simulation environments and data pipelines, collaborating with hardware and firmware teams.
What they ask for
- Hands-on experience training reinforcement or imitation learning policies that run on real systems.
- Comfort working directly with robots, not just simulators.
- Proficiency in Python and familiarity with standard RL/ML frameworks.
- Empirical, debugging-first approach and ability to move quickly between research and engineering tasks.
Nice to have
- Prior work on humanoid or legged robot platforms.
- Experience with sim-to-real transfer techniques, open-source robotics, control theory, trajectory optimization, or dynamics.
Tools
Python, JAX, PyTorch, IsaacGym/IsaacLab, MuJoCo
AI focus
Designs, trains, and deploys reinforcement and imitation learning policies for real humanoid robots.
Model-written summary · the original posting is authoritative