InLoop builds warehouse robots that run fully autonomously in customer warehouses today. The fleet is growing quickly (20+ robots), and the demand is huge.
We're hiring a research engineer to make our robots learn precise, contact rich tasks in simulation and carry them over to real hardware. You will be working on the real customer use-cases, and getting the best feedback, directly in a field.
This is research that ships. What you train runs on real robots in weeks, not years.
What you'll work on
- Manipulation with RL. Train reinforcement learning controllers for precise manipulation tasks.
- Sim to real. Get policies trained in simulation working on our real arms, and close the gap when they don't.
- Control. Combine learned policies with classical and learned control where it makes the robot more precise or safer.
- Into the stack. Bring what works into our autonomy stack so it runs on the fleet.
What we're looking for
- Hands on RL for robot control, trained in simulation and run on real hardware
- Large scale simulation (Isaac Sim, Isaac Lab, MuJoCo or similar)
- Strong control fundamentals
- A track record of shipping: code on real robots, papers or open source
Nice to have
- Experience with Vision language action models or imitation learning
- Experience with Code-as-a-Policy
- PhD with a track record of publications (CVPR, ICRA, ICLR, CORL, and etc.)
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