Research Engineer

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

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

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.