You’ve already trained and deployed learning-based policies on real robots. Now you can help bring the next generation of humanoid robotics into the real world.
You’ll join an ambitious robotics company developing general-purpose humanoids, building robot-learning systems that work beyond the simulator. You’ll train and improve policies, develop data pipelines, run experiments on physical hardware and tackle the challenges of reliable sim-to-real transfer.
This is a hands-on engineering role for someone who combines strong machine-learning fundamentals with practical robotics experience. You’ll work across imitation learning, reinforcement learning and emerging vision-language-action models, helping turn promising approaches into robust robotic capabilities.
You’ll need:
- Minimum 2+ years professional experience training real robot policies using reinforcement learning, imitation learning or learning from demonstration.
- Proven experience deploying learned policies on physical robots.
- Strong Python and PyTorch skills, with good C++.
- Experience with robot data collection, simulation and sim-to-real transfer.
- A solid understanding of robot control, action representations and closed-loop policy execution.
If you want to turn advances in robot learning into capable, reliable humanoid robots, let’s chat!
