DiDi
DiDi is a mobility technology platform offering app-based ride hailing, taxi hailing, shared mobility, food delivery, and intra-city freight to consumers and businesses across Asia Pacific, Latin America, and other markets. It connects riders with drivers and couriers and provides vehicle, energy, and insurance-related services, earning transaction commissions and service fees. DiDi launched an autonomous driving unit in 2016, which became the independent DiDi Autonomous Driving in 2019 to develop Level 4 technology for its network.
Open Positions at DiDi
7 open positions
Develop ML-powered motion-planning systems for Level 4 autonomous vehicles, using real-world driving data, learned planners, and end-to-end models. This role combines machine learning research with production deployment under strict safety, latency, and onboard-compute constraints.
Lead DiDi Autonomous Driving’s company-wide shift to an AI-Native engineering organization by architecting large-scale LLM, ML, and distributed computing platforms. This hands-on strategic role combines systems architecture, AI infrastructure optimization, and embedded execution with autonomous driving engineering teams.
Lead the architecture and evolution of infrastructure powering DiDi’s autonomous driving software stack, including middleware, compilers, toolchains, and simulation platforms. This senior technical leadership role focuses on low-latency systems, developer productivity, crash diagnostics, and embedded autonomy software.
Develop core motion-planning and decision-making systems that enable DiDi’s autonomous vehicles to navigate complex environments safely, smoothly, and intelligently. The role spans behavioral reasoning, trajectory optimization, geometry, and velocity planning.
Join DiDi Autonomous Driving as a Motion Planning Engineer Intern and develop planning algorithms for Level 4 autonomous vehicles. You will work on behavioral planning, trajectory optimization, and safe motion generation for complex driving scenarios.
Lead AI infrastructure performance, inference optimization, deployment, and resource scheduling for autonomous-driving models across vehicle-side and cloud environments. The role bridges deep learning algorithms with real-time embedded systems to deliver low-latency, reliable AI execution.
Develop core behavioral planning and motion-planning systems that enable Level 4 autonomous vehicles to navigate safely and intelligently. The role spans high-level decision-making, trajectory optimization, geometry, and velocity planning.