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 building the systems that let Asimov pick up a box, open a drawer, and operate tools. As a Robotics Researcher in Manipulation, you will develop the grasp planning, contact-rich control, and learned task policies that power Asimov's hands. You will work across model-based control, imitation learning, and reinforcement learning -- with the bar set by whether it works on the physical robot in a real environment, not just in simulation. This role combines research depth with a relentless focus on shipping to hardware.

What You Will Do

• Research, develop, and deploy manipulation policies for dexterous task execution on Asimov
• Build grasp planning and contact-rich control pipelines that generalize across varied objects and environments
• Design and run data collection and teleoperation infrastructure to feed policy training at scale
• Train manipulation policies using imitation learning, reinforcement learning, or hybrid approaches -- and iterate until they work in the real world
• Integrate manipulation with Asimov's perception stack and broader autonomy pipeline
• Systematically diagnose failure modes on hardware and drive improvement
• Contribute to open-source releases of manipulation research and tooling
  

What You Will Bring

• Strong foundations in robotics, control theory, and motion planning
• Hands-on experience building and deploying manipulation systems on real robotic platforms
• Proficiency in Python and C++; experience with PyTorch or JAX
• Track record taking manipulation research from prototype to hardware deployment
• Experience with data collection infrastructure and teleoperation for policy training
• Practical debugging instincts across the full hardware-software stack
  

Nice to Have

• Experience with diffusion policies, transformer-based policy architectures, or large-scale foundation models for manipulation
• Prior work on dexterous or in-hand manipulation
• Familiarity with contact-rich or deformable object manipulation
• Publications at RSS, ICRA, CoRL, or equivalent venues
  

Why Join Menlo

This is applied robotics research with real stakes -- your code runs on a physical humanoid. We open-source aggressively, so your contributions reach the broader community. You will work alongside researchers and engineers across the full stack, in a team that values shipping over presenting. Competitive compensation and equity.