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.) **How to apply** Apply here and include a link to a policy you trained running on a real robot, a paper or a repo.
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Robotic arms that pick and pack your orders.