Founding Machine Learning - Eval Layer
About the role
One Robot is hiring for a role focused on building the evaluation layer for robot manipulation policies. The team develops world models and an evaluation platform to validate and improve end-to-end robotics policies, emphasizing rigorous validation over trial-and-error and reducing the sim-to-real gap.
Responsibilities
- Train evaluation models: Develop VLMs that classify and verify policy behavior.
- Build confidence layers: Convert model outputs into trustworthy signals the customer can act on.
- Improve model grounding: Make the eval models reason accurately about physical and spatial scenes.
- Build a self-improving eval layer: Develop data engine that makes the eval models sharper with each customer's deployments and corrections.
Qualifications
- Very strong coding in Python and PyTorch.
- VLM/LLM training: Track record in training VLMs or LLMs.
- Evals experience: Developed and shipped evals for VLMs or LLMs.
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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