Founding Software Engineer, Robot Learning

The Role

You will own Kovari's perception stack end-to-end—from raw sensor data to actionable representations for both learned policies and classical control. Your systems will run on deployed robots in real hotel environments, handling the messy realities of variable lighting, glass surfaces, temporary obstacles, and repetitive architecture.

• Research and develop high-reliability manipulation policies designed for high-velocity deployment and iteration
• Operate in a fast data flywheel across multiple data modalities
• Deep debug failure modes in transformer and diffusion policy field deployments
• Optimize policies for real-time (~10hz) inference on edge hardware

• Experience deploying robot policies on hardware. No preference between model-based learning, reinforcement learning, or imitation learning
• Sim-to-real or real robot data
• Experience building policies with multimodal inputs (vision, depth, force/torque, proprioception)
• Experience with deep optimizations for constrained edge devices TensorRT, ONNX Runtime, or TVM for inference optimization
• CUDA kernel optimization
• Ideally, contributions at major robotics/ML conferences (CoRL, RSS, ICRA, NeurIPS)

• Pace of learning trumps everything else.
• Refining our craft is something we pursue relentlessly.
• Low ego, high ownership.
• Commitment to the mission. We work in-person, and this isn't a 9-to-5. We're building something hard, and we need people who are all-in.