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About the Role

One Robot is building an evaluation platform for robot manipulation policies, replacing trial-and-error with rigorous validation to identify policy failures and guide data collection for improvement. The company is solving challenging technical problems in long-horizon autoregressive generation, world model controllability, and closing the sim-to-real gap, working with real customer data and deployment pressure.

Responsibilities

Train manipulation policies, including VLAs, diffusion policies, or end-to-end imitation models, and run them on real robots.
Validate the world model end-to-end by training policies in simulation, deploying on real hardware, and identifying transfer issues.
Build the infrastructure that enables policies to train and improve on the platform, pushing policy capabilities forward.

Requirements

Very strong coding skills in Python and PyTorch.
Proven track record of training manipulation policies that have run on physical hardware.
Hands-on experience curating real-robot demonstration datasets.

Location

San Francisco

Work Type

Full-time

About the Company

One Robot builds task-specific world models and an evaluation platform for robot manipulation policies.
The company is based in San Francisco and is backed by Accel, YC, several exited founders, and engineering leaders at leading AI companies.
Founded by Hemanth Sarabu and Elton Shon, who previously led robot learning at Industrial Next (YC W22) and have experience from Google, NASA JPL, and Tesla.
The company is small and emphasizes deep IC ownership, fast iteration, and direct responsibility.