Founding Machine Learning - World Models
About the role
Role focused on building world models to simulate manipulation scenes for validation and future policy training, with emphasis on generative models, long-horizon coherence, and scalable training infrastructure.
Responsibilities
- 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.
Qualifications
- 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.
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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