Founding Software Engineer, Perception
About The Position
At Kovari, we're rethinking how physical work gets done in the age of robotics. We believe building robots that can move the economy is one of the most important endeavors in technology. Our first goal is to build general-purpose robots for hospitality to take on physical, repetitive work that keeps the hospitality industry operating. The last mile problem for proliferating useful robots into businesses is a first class innovation problem itself. We aim to marry deep commercial understanding with fast paced innovation to create robots that move the industry. Since inception, we have raised over $6M to carry out our mission from industry leading investors. We are obsessed with rapid iteration, engineering rigor, and deploying real machines into real environments. The next decade will compress a century of progress in robotics, and we're looking for people who want to leave their fingerprints on that future. We are based in San Francisco and work in-person.
Requirements
- Experience deploying robot policies on hardware (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.
Nice To Haves
- Contributions at major robotics/ML conferences (CoRL, RSS, ICRA, NeurIPS).
Responsibilities
- Own Kovari's perception stack end-to-end—from raw sensor data to actionable representations for both learned policies and classical control.
- Develop systems that 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.
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What This Job Offers
Job Type
Full-time
Career Level
Entry Level
Education Level
No Education Listed
