Teams at DiDi

Lead company-wide AI transformation by architecting distributed AI platforms for data processing, model training, inference, and evaluation. Embed with autonomous driving engineering teams to remove bottlenecks and deliver AI DevOps and copilot ecosystems. Design LLMOps/MLOps automation, optimize GPU clusters and inference performance, guide technical roadmaps, and drive adoption of AI-native engineering practices across infrastructure, algorithms, and hardware teams.

Lead safety strategy and deployment readiness for Level 4 autonomous driving systems. Define safety requirements, system architectures, release policies, fallback criteria, V&V plans, evaluation metrics, emergency protocols, and operational safety processes. Analyze incidents, improve test coverage and procedures, and collaborate with software, product, operations, and safety teams to support unmanned deployment and commercial scaling.

Develop core decision-making and motion planning systems for Level 4 autonomous vehicles. Responsibilities include behavioral planning, path and velocity generation, trajectory optimization, world modeling, trajectory cost design, testing, debugging, and root-cause analysis. The engineer will collaborate with Prediction, Perception, and Control teams to deliver safe, comfortable, and intelligent vehicle motion in complex environments.