The role involves leading and building a team of machine learning engineers focused on sensing anomaly detection and calibration. The individual will develop a data flywheel to automatically generate training data for sensor degradation events using vision language models. The position requires owning the entire sensor validation stack from end to end. Additionally, the role entails driving collaborations between perception, system engineering, evaluation, and other internal teams. The work contributes to ensuring that sensors function reliably across the entire autonomous vehicle software stack.

Candidates must possess a Master of Science degree in Computer Science, Robotics, Math, Physics, or equivalent industry experience. The role requires at least six years of experience in designing, training, and deploying machine learning-driven real-time robotics systems. Applicants need four years of experience leading technical teams and two years of people management experience. A PhD in a relevant technical field is preferred.