The RoleYou will research and ship manipulation policies that work reliably in the field. You own the loop from data to model to deployment, and you see your policies run on robots in live operations.
What You'll Do- Research and develop high-reliability manipulation policies built for fast deployment and iteration
- Operate in a fast data flywheel across multiple modalities
- Deep debug failure modes in transformer and diffusion policies deployed in the field
- Optimize policies for real-time (~10 Hz) inference on edge hardware
What You Bring- Experience deploying robot policies on hardware (model-based, RL, or imitation learning)
- Sim-to-real or real robot data experience
- Experience with multimodal inputs: vision, depth, force/torque, proprioception
- Edge inference optimization with TensorRT, ONNX Runtime, or TVM
- CUDA kernel optimization
- Bonus: publications at CoRL, RSS, ICRA, NeurIPS, or similar
- Bias toward action: train fast, deploy early, learn from failure
Values- Pace of learning trumps everything.
- Relentless refinement of our craft.
- Low ego, high ownership.
- All-in on the mission. We work in person, and this isn't a 9-to-5.
