Senior AI Engineer Self-Supervised Learning

Our global fleet of autonomous robots  operates in the real world, generating vast amounts of multi-modal sensor data. While our VLA team focuses on building large-scale models to consume this data, much of it remains unlabeled and unstructured. We are seeking an expert in self-supervised and representation learning to unlock the full potential of this massive data pool.

In this role, you will be responsible for designing and building the core data engine that transforms raw, real-world sensor data into high-signal, structured datasets suitable for training neural networks. You will pioneer methods to automatically curate, filter, and pseudo-label this data, creating powerful representations that serve as the foundation for all downstream tasks, including navigation, imitation learning, and decision-making. 

You will work directly with the VLA and Reinforcement Learning teams to define data strategies and interfaces, ensuring the data you produce directly accelerates their model development. If you are passionate about solving the "data bottleneck" in robotics and want to build the systems that learn meaningful patterns from the physical world, we invite you to join us.