The role focuses on advancing operational efficiency for an autonomous vehicle fleet through the application of robotics. Responsibilities include developing solutions for depot tasks such as vehicle cleaning, sensor calibration, maintenance checks, and inspections. The position involves leveraging foundation models, reinforcement learning, and simulation to solve complex problems. Integration of machine learning models into production environments at scale is a primary objective. Collaboration with operations teams is required to translate real-world requirements into functional technical solutions.

Candidates must possess at least three years of experience in training and evaluating large machine learning models. A minimum of three years of experience in robotics, specifically industrial robotics, is required. Expertise in reinforcement learning and its application to real-world problems is necessary. A PhD in Machine Learning, Robotics, or a related technical field is preferred, though equivalent experience is accepted.