Founding Software Engineer, Robot Learning

Kovari; San Francisco (CA); On-site; USD 95,000 - 120,000; Full time

Job summary

Kovari in San Francisco is seeking a passionate individual to own the perception stack for robotic systems. You will engage in high-reliability policy development and optimization in variable real-world environments.

The ideal candidate has experience with deploying robot policies, multimodal inputs, and real-time inference on edge hardware. This role demands dedication and commitment, as we aim to achieve challenging goals together.

Qualifications

• Experience deploying robot policies on hardware, including model-based, reinforcement, or imitation learning.
• Strong background in sim-to-real robotics and multimodal sensor data.
• Ability to optimize policies for real-time inference on edge hardware.

Responsibilities

• Own Kovari's perception stack from raw data to actionable representations.
• Research and develop high-reliability manipulation policies.
• Deep debug failure modes in policy field deployments.
• Optimize policies for real-time inference on edge hardware.

Skills

Experience deploying robot policies on hardware

Building policies with multimodal inputs

CUDA kernel optimization

Deep optimization for constrained edge devices

Contributions at robotics/ML conferences

Job description

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.

What You'll Do

• 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

What you bring

• 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)

Values

• 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.