We build world models that simulate manipulation scenes faithfully enough to validate, and one day, train policies without touching a robot. You'll develop generative models that make this work, with the controllability and physical fidelity to match real-robot behavior.
What you'll do:
Requirements:
One Robot builds task-specific world models and an evaluation platform for robot manipulation policies.
Training end-to-end policies for robots is vibes-based today. Teams collect data, train, deploy on a real robot, find out what fails, collect more, retry. We replace the trial-and-error with rigorous validation that tells you where your policy will fail and what data to collect to fix it.
Robotics can't industrialize without an evaluation layer. We're building it.
We're solving challenging technical problems around long-horizon autoregressive generation, world model controllability, and closing the sim-to-real gap. We work with real customer data, real failures, and real deployment pressure.
We're based in San Francisco, backed by Accel, YC, several exited founders, and engineering leaders at leading AI companies.
We're small and deliberately so. Everyone is an IC with deep ownership of a wide surface area. The culture is fast iteration and direct responsibility.
Hemanth Sarabu and Elton Shon co-founded One Robot after leading robot learning together at Industrial Next (YC W22), bringing experience from Google, NASA JPL, and Tesla.
Compensation: $150K - $275K
Experience: Any (new grads ok)
