Machine Learning Engineer - Agentic AI

Autonomous driving software today improves mostly by hand: engineers collect data, train models, and tune modules. This role explores a different approach, in which AI agents take part in that work. Agents propose changes to policy code, data, training recipes, or model code. They then evaluate the results, diagnose failures, and turn verified fixes into reusable skills and data for the next round. With DiDi Voyager's real-world driving data and large-scale evaluation infrastructure, the goal is a loop that produces improvements that generalize across scenarios, pass safety gates, and reach the vehicle.

The role combines machine learning, agentic systems, and autonomous driving, and will help shape both the onboard architecture and how driving software is developed.