Founding Robot Learning
About the role
One Robot is hiring for founding engineers to build and scale world models and evaluation platforms for robot manipulation policies. The role focuses on training manipulation policies, validating world models end-to-end, and pushing policy capabilities forward on a platform that bridges simulation and real hardware.
Responsibilities
- Train manipulation policies: Build VLAs, diffusion policies, or end-to-end imitation models and run them on real robots.
- Validate the world model end-to-end: Train policies in simulation, deploy on real hardware, and identify transfer gaps.
- Push policy capabilities forward: Build infrastructure that enables policies to train and improve on our platform.
Qualifications
- Very strong coding in Python and PyTorch.
- Real-robot policy training: Track record training manipulation policies that ran on physical hardware.
- Demonstration data: Hands-on experience curating real-robot demonstration datasets.
Skills
About One Robot
World models for robot evals and training.
One Robot builds simulation environments that are realistic to see and realistic to interact with, so robotics teams can train and evaluate robot policies without being bottlenecked by robot time. Today, improving a VLA often means more real-world hours: setting up the scene, running trials, resetting, and repeating. This loop is slow, expensive, and hard to scale. For example, material handling and manufacturing assembly tasks, models need far more training and evaluation data than teams can collect in the real world. We use task-specific data to build world model-based simulation environments for hard manipulation tasks (for example, textiles and box folding). These environments help teams run more training and evals, find failure modes faster, and accelerate iteration on action policies with less dependence on real-world data collection and robot availability.
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