Teams at DiDi

Lead the architecture and evolution of infrastructure powering autonomous driving software, including low-latency IPC middleware, runtime engines, compiler and build toolchains, simulation platforms, crash diagnostics, and developer productivity systems. Drive performance profiling, memory optimization, fault isolation, and cross-team engineering standards while mentoring engineers and guiding technical architecture across autonomy and simulation teams.

Lead company-wide AI transformation by designing unified distributed AI platforms, integrating frontier LLM ecosystems, and embedding with autonomous driving engineering teams. Build scalable LLMOps and MLOps pipelines, optimize GPU infrastructure and inference performance, develop AI-native engineering tools, and establish technical roadmaps. The role combines hands-on systems engineering, enterprise AI platform development, cross-functional influence, and strategic leadership across perception, prediction, planning, control, and simulation teams.

Conduct research on scene understanding and planning for autonomous vehicles. Design, train, evaluate, and debug deep learning models using large-scale driving data and sensor inputs. Develop metrics, perform failure analysis, survey research, and integrate validated prototypes into production planning systems while collaborating with perception, prediction, planning, mapping, and engineering teams.