Teams at DiDi

PhD-level intern to research and implement motion and behavioral planning algorithms for autonomous vehicles. Tasks include trajectory and velocity planning, multi-agent interaction modeling, cost-function optimization, simulation and real-world testing, and cross-team integration with Perception, Prediction, and Control.

Lead design and implementation of a distributed AI platform and LLM deployment pipelines. Embed with autonomous-driving teams to remove engineering bottlenecks, optimize GPU cluster utilization and inference latency, and drive enterprise AI-native transformation from architecture through production deployment and monitoring.

Lead safety strategy and deployment readiness for unmanned autonomous driving systems. Define release policies, safety requirements, system architecture, and V&V plans. Conduct assessments of operational workflows and emergency protocols, develop safety metrics and evaluation methodologies, lead incident analysis, and collaborate cross-functionally to enable safe, scalable unmanned operations.