> Markdown version of [/jobs/ext/3319046-researcher-locomotion](https://www.wearedevelopers.com/jobs/ext/3319046-researcher-locomotion). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Researcher, Locomotion

- **Company:** Menlo, Inc.
- **Location:** Palo Alto, CA, United States
- **Contract:** Permanent contract
- **Skills:** Software Debugging, Python (Programming Language), Open Source Technology, Reinforcement Learning
- **Published:** September 3, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=ff3dcef7f4f1083c

## About the Role

* Deep hands-on experience with reinforcement learning for continuous control, legged locomotion, or whole-body control
 * A track record of getting learned policies onto real robots, not only into papers or simulators
 * Strong command of a physics simulator such as MuJoCo, Isaac, or similar, including 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 see a policy stumble on real hardware this week than look perfect in sim next month
 * Clear thinking about why a policy fails, not just whether it does

Nice to Have

 * Published work in locomotion, legged robotics, or sim2real transfer
 * Experience with model predictive control or classical locomotion methods alongside learning-based approaches
 * Contributions to open-source robotics or RL projects
 * Experience bringing up new hardware and debugging the messy gap between a model and a motor

## Description

We are looking for a Researcher, Locomotion to push Asimov from walking to running, recovering, and moving through the real world with the confidence of something alive. Asimov 0 was built to learn locomotion and Asimov 1 to learn whole-body control, and you will own the policies that make that motion robust on real hardware. You will not stop at a clip in simulation. You will get your work onto a physical biped and keep pushing until it holds up under contact, disturbance, and terrain we did not train for.

What You'll Do

 * Design, train, and ship reinforcement learning policies for bipedal and whole-body locomotion on Asimov
 * Own the sim2real pipeline end to end, building on our zero-shot sim2real work so a first run on hardware is never really a first run
 * Push balance, gait, and recovery behavior past the demo stage into something that survives pushes, slips, and uneven ground
 * Build and refine reward design, domain randomization, and training environments in MuJoCo
 * Close the loop between simulation and hardware with real telemetry from real robots, then feed what breaks back into the next policy
 * Work shoulder to shoulder with hardware, controls, and manipulation researchers, since whole-body control does not respect team boundaries
 * Open-source what you can and write up what you learn so the community can build on it


## Related Videos

- [Adding knowledge to open-source LLMs](https://www.wearedevelopers.com/videos/1522-adding-knowledge-to-open-source-llms)
- [On the straight and narrow path - How to get cars to drive themselves using reinforcement learning and trajectory optimization](https://www.wearedevelopers.com/videos/205-on-the-straight-and-narrow-path-how-to-get-cars-to-drive-themselves-using-reinforcement-learning-and-trajectory-optimization)
- [Embracing the Hybrid Cloud: Unlocking Success with Open Source Technologies](https://www.wearedevelopers.com/videos/883-embracing-the-hybrid-cloud-unlocking-success-with-open-source-technologies)
- [Using AI Without Losing Your Skills](https://www.wearedevelopers.com/videos/2045-using-ai-without-losing-your-skills)
- [Robots Among us: Advances in AI for Everyday Androids](https://www.wearedevelopers.com/videos/100230-robots-among-us-advances-in-ai-for-everyday-androids)
- [How Robots Learn to be Robots](https://www.wearedevelopers.com/videos/1632-how-robots-learn-to-be-robots)

## Related Articles

- [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production)
- [Dev Digest 137 - AI'm not sure about this](https://www.wearedevelopers.com/magazine/485-dev-digest-137-ai-m-not-sure-about-this)
- [ I Gave a Video Editor More Autonomy Than a Trading Bot. On Purpose.](https://www.wearedevelopers.com/magazine/773-i-gave-a-video-editor-more-autonomy-than-a-trading-bot-on-purpose)
- [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)
- [AI Eats the Verifiable First](https://www.wearedevelopers.com/magazine/765-ai-eats-the-verifiable-first)
- [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)