Locomotion Researcher, Open-Source Humanoids

Hyphen Partners; Singapore; On-site; SGD 120,000 - 150,000; Full time

Job summary

Hyphen Partners is partnering with an Applied R&D lab focused on open-source humanoid robot platforms. They seek a Locomotion Researcher to develop motion capabilities from walking to running with real hardware.

You will own the sim2real pipeline, push robustness, design reward shaping in MuJoCo, and work across hardware, controls, and manipulation researchers. You will open-source what you learn.

Qualifications

• Deep hands-on experience with reinforcement learning for continuous control, legged locomotion, or whole-body control.
• Track record of getting learned policies onto real robots.
• Strong command of a physics simulator such as MuJoCo or Isaac.
• Fluency in Python and modern RL tooling, and comfort in a ROS2-based control stack.
• Bias for shipping: prefer real hardware progress this week over perfect in simulation.
• Clear thinking about why a policy fails, not just whether it does.

Responsibilities

• Design, train, and ship reinforcement learning policies for bipedal locomotion and whole-body control.
• Own the sim2real pipeline end to end, building on zero-shot sim2real work.
• Push balance, gait, and recovery beyond the demo and into real-world robustness.
• Build and refine reward design, domain randomization, and training environments in MuJoCo.
• Close the loop between simulation and hardware with real telemetry from robots.
• Collaborate with hardware, controls, and manipulation researchers.
• Open-source what you can and document learnings for the community.

Skills

Reinforcement learning for continuous

Legged locomotion

Real-robot policy deployment

Python

ROS2

Tools

MuJoCo

Isaac

ROS2

Job description

Hyphen Partners is partnering with an Applied R&D lab focused on open-source humanoid robot platforms. They seek a Locomotion Researcher to develop motion capabilities from walking to running with real hardware.

You will own the sim2real pipeline, push robustness, design reward shaping in MuJoCo, and work across hardware, controls, and manipulation researchers. You will open-source what you learn.