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'll start on robotic assembly and work directly with our CTO.
This is research that ships. What you train runs on real robots in weeks, not years.
**What you'll work on first**
\- **Manipulation with RL.** Train reinforcement learning controllers for precise manipulation tasks.
\- **Simulation at scale.** Build the environments and randomization to train fast and in parallel.
\- **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 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
\- Vision language action models or imitation learning
\- Strong Python and PyTorch
\- A track record of shipping: code on real robots, papers or open source
**Nice to have**
\- Contact rich manipulation
\- PhD with a track record of publications (CVPR, ICRA, ICLR, CORL, and etc.)
\- German
**How to apply**
Apply here and include a link to a policy you trained running on a real robot, a paper or a repo. That tells us more than a CV.
Visa sponsorship is offered for this role.
