Staff Systems Engineer - Camera

About the role

About 1X

We're building humanoid robots that work in home - doing the chores, handling the tasks, and giving people their time back. Simple, but it's not.

To do this right, we have to solve robotics, AI, manufacturing - at the same time, at scale, in a form factor that has to be safe enough to live with your family. If you're inspired by this, you'll thrive here. We've been at this since 2014 and we're at the point where the hard problems are behind us and the hard work is in front of us.

NEO is our flagship - a home robot designed to move, learn, and operate in the real world alongside real people. We're not demoing it - we're shipping it. We're excited to meet you, if this excites you.

If you've spent your career working on problems that matter and want to see them actually reach the world - this is that moment. We're scaling, we're hiring with intention, and we need people who want to build something that will genuinely change how humans spend their time - safely creating abundance for all.

About the Team

The Core team builds and owns the core C++ infrastructure that NEO’s entire stack runs on, from sensor pipelines, motor control interfaces, networking, inter-process communication, and the foundational libraries that ship on every robot. Our code executes thousands of times per second on hardware moving through the real world, and its correctness and performance directly determines what every other team can build and ship. If you want to work on the systems layer that makes physical AI possible at scale, this is that team.

Your Charter

Own the camera stack NEO sees through, from the sensor and lens selected for the robot to the calibrated, rectified frames every perception model consumes. This is critical-path work: no vision model is better than the images it is handed, and nothing downstream survives a camera that is miscalibrated, badly tuned, or quietly dropping frames. You will own that path from vendor selection through end-of-line calibration on the production line and out to robots already working in homes.

Key Outcomes

- Own camera selection end to end: define sensor, lens, and module requirements with the hardware and perception teams, run the trade studies, and carry a choice through vendor engagement and bring-up on real hardware

- Deliver a camera pipeline that holds up in production: sensor drivers, multi-camera synchronization and timestamping, and frame delivery that meets its latency budget without dropping frames or destabilizing timestamps

- Tune the image path for machines rather than for people: exposure, white balance, and dynamic range chosen so perception models are accurate in real homes, including the low light and hard backlight a living room actually presents

- Make calibration a production process, not a lab procedure: intrinsics and extrinsics calibrated on the line inside cycle time, validated automatically, monitored for drift across the fleet, and recoverable in the field