Teams at DiDi

Develop map fusion and motion planning software for autonomous vehicles. Responsibilities include integrating HD maps with real-time perception, building BEV mapping networks, implementing trajectory planning and optimization algorithms, deploying deep learning models through ONNX and TensorRT into C++ runtimes, detecting map anomalies, and optimizing safety-critical system performance, latency, memory, and multithreading.

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.