Company Description
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.
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
- Build perception systems for manipulation tasks and localization/navigation
- Optimize camera pipelines for low latency, high-frequency performance suitable for both policy consumption and classical robotics stacks
- Develop real-time object detection, segmentation, and pose estimation for live deployed robots
- Solve environment-specific challenges: dynamic obstacles, lighting variation, repetitive textures, reflective surfaces
- Debug perception failure modes on deployed hardware in the field
What you bring
- Deep C++ and systems knowledge with experience optimizing camera frame pipelines for low-latency, high-hz perception
- Strong background in classical computer vision: extrinsic calibration, SLAM, visual odometry, point cloud registration
- Experience with learned perception: detection, segmentation, depth estimation, pose estimation
- Sensor fusion and state estimation expertise (Kalman filters, factor graphs)
- Track record building safety-critical perception systems on deployed hardware
Values
- The single most important factor for success is pace of learning.
- 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.
