Founding Machine Learning - World Models

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.

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:

• Train video and dynamics models: Develop world models with action conditioning for manipulation policies.
• Push long-horizon coherence: Develop architectures and training methods that extend rollout quality on hard physical tasks.
• Own training infrastructure: Run multi-GPU clusters, write custom CUDA, debug at scale.
• Build the world-model data engine: Design, implement, and improve a data engine that allows the world model to compound learning across customers and manipulation tasks.

Requirements:

• Very strong coding in Python and PyTorch (or similar).
• Video generation experience: Deep experience training image or video generation models end-to-end.
• Large-scale training: Track record operating training runs at cluster scale.
• 3D vision: Working knowledge of multi-view geometry, scene reconstruction, and physical priors.