Teams at DiDi

Lead company-wide AI-native transformation by designing and implementing a unified LLM/AI platform. Embed with autonomous driving teams to operationalize LLMs, optimize distributed training and inference, improve GPU utilization, and build resilient MLOps/LLMOps pipelines while setting technical roadmap and driving cross-functional adoption.

Lead deployment, optimization, and resource scheduling of vehicle and cloud AI models. Design low-latency inference pipelines, build system stability and profiling frameworks, optimize GPU execution, and scale LLM serving for simulation and validation. Collaborate with perception, cloud, and safety teams to enable reliable, production-grade autonomous deployment.

Design and implement map fusion and motion planning software for autonomous vehicles. Build data pipelines, integrate HD maps with real-time perception, deploy DETR-style BEV mapping networks, develop trajectory generation and optimization, create anomaly detection for map reliability, and optimize low-latency, multi-threaded C++ runtime for safety-critical planning systems.