Researcher, Manipulation

Menlo; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Menlo is seeking a Researcher, Manipulation to advance the Asimov humanoid platform. You will drive progress in grasping, dexterous and contact-rich tasks, coordinating arms, torso, and legs to solve challenges in real hardware.

You will design, train, and deploy RL policies, improve sim2real transfer, and collaborate with locomotion, controls, and hardware teams while contributing to open-source efforts.

Qualifications

• Hands-on experience with reinforcement learning for manipulation and dexterous control.
• Track record of deploying manipulation policies on real robots, not only in simulation.
• Strong programming skills in Python and ROS2-based control stacks.
• Proficiency with physics simulators (MuJoCo, Isaac) including contact modeling and domain randomization.

Responsibilities

• Design, train, and ship RL policies for manipulation, grasping, and dexterous tasks on Asimov.
• Extend RL work toward whole-body manipulation with balance and reaching integrated.
• Own the sim2real pipeline for manipulation to ensure transfer to hardware.
• Design rewards and training environments, including randomization strategies in MuJoCo.

Skills

Reinforcement Learning

Manipulation

Python

MuJoCo

ROS2

Tools

MuJoCo

ROS2

Isaac

Job description

About Menlo

Menlo Research is an Applied R&D lab building Asimov, an open-source humanoid robot platform, and the full software stack that powers it. Our mission is to make humanoid labor economically viable, turning software into physical labor at scale. We build across the full stack: hardware architecture, locomotion, autonomy, simulation, and infrastructure. We move fast, ship to real robots, and open-source everything we can. If you want your work to matter beyond a paper or a demo, this is the place.

The Role

We are looking for a Researcher, Manipulation to give Asimov hands that can do real work. We recently ported our RL stack from locomotion to manipulation, taking Asimov from walking to flipping, and you will drive what comes next: grasping, dexterous and contact-rich tasks, and the kind of whole-body coordination where an arm, a torso, and two legs have to solve one problem together. You will get skills onto real hardware and keep pushing until they hold up outside a controlled setup.

What You'll Do

• Design, train, and ship reinforcement learning policies for manipulation, grasping, and dexterous, contact-rich tasks on Asimov

• Build on our locomotion-to-manipulation RL work and extend it toward whole-body manipulation, where balance and reaching are one problem

• Own the sim2real pipeline for manipulation, using our zero-shot sim2real approach so skills transfer to hardware cleanly

• Design rewards, training environments, and randomization strategies in MuJoCo for contact-heavy tasks that are hard to simulate well

• Close the loop with real telemetry from real arms and hands, then feed failures back into the next policy

• Collaborate closely with locomotion, controls, and hardware researchers, since a humanoid manipulates with its whole body

• Open-source what you can and share what you learn with the community building on Asimov

What We Look For

• Deep hands-on experience with reinforcement learning or learning-based methods for manipulation, grasping, or dexterous control

• A track record of getting manipulation policies onto real robots, not only into papers or simulators

• Strong command of a physics simulator such as MuJoCo, Isaac, or similar, including contact modeling, reward shaping, and domain randomization

• Fluency in Python and modern RL tooling, and comfort in a ROS2-based control stack

• A bias for shipping: you would rather watch a policy fumble a real object this week than perfect it in sim next month

• Clear thinking about why contact-rich tasks fail, and how to close the gap

Nice to Have

• Published work in manipulation, dexterous control, grasping, or imitation learning

• Experience with tactile sensing, force control, or teleoperation and demonstration data

• Experience with whole-body control that spans locomotion and manipulation

• Contributions to open-source robotics or RL projects

Why Join Menlo

You will own manipulation for a humanoid that thousands of developers are building on in the open. Your skills will ship to real robots and real hands, not slides, and the community will see them work. If you want your research to reach into the physical world and pick things up, this is the place.

A Note on AI

You don't need deep AI expertise for every role, but we do expect everyone at Menlo to be intellectually curious, drawn to tinkering and discovery, and excited to use AI as a real collaborator in their work. For some roles, AI fluency is a core requirement. When that's the case, we'll say so explicitly in the qualifications. People who thrive here don't treat AI as a novelty. They use it to think better, and make their work easier for others to build on.

Equal Opportunity and Accommodations

We hire talented people from a wide range of backgrounds. If you're excited about a role but don't meet every bullet, we still encourage you to apply. Menlo Research is an equal opportunity employer and does not discriminate on the basis of any legally protected characteristic. Menlo provides reasonable accommodations during the application process. If you need one, please let your recruiter know.