Description
The role develops, trains, and optimizes deep-learning models for autonomous driving, including perception, online mapping, and end-to-end planning. It covers the full machine-learning lifecycle from data curation and analysis through experimentation, hyperparameter tuning, and performance verification, while collaborating with simulation, infrastructure, and planning/control teams to integrate models into production pipelines. The position requires a quantitative degree, neural-network training experience, strong machine-learning and deep-learning knowledge, Python and PyTorch proficiency, and software-engineering fundamentals; computer vision, robotics research, model-deployment experience, and autonomous-driving industry exposure are preferred.
