$ cat jobs/software-engineer-sr-software-engineer-planning-selection-au-didi-labs-cefd5c3706f2.json
Software Engineer / Sr. Software Engineer, Planning Selection (Autonomy)
About the Company DiDi's autonomous driving unit was established in 2016 with the mission of developing Level 4 autonomous driving (AD) technology to make transportation safer and more efficient. In August 2019, the unit became an independent company, DiDi Autonomous Driving, dedicated to advanced AD R&D, product application, and business expansion. We believe integrating AD technology into a shared-mobility fleet will generate immense social value. By leveraging DiDi's specialized technology, operational expertise, and integrated ecosystem, we are positioned to build and operate a highly efficient, user-oriented autonomous fleet. About The Role We are seeking a talented and mission-driven Software Engineer / Sr. Software Engineer, Planning Selection to contribute to the development of our core planner engine for decision-making, trajectory generation, and trajectory evaluation . In this role, you will apply both classic robotics behavior planning and modern machine learning algorithms to evaluate candidate trajectories, build multi-objective cost functions, and help select safe, comfortable, and e fficient t rajectories for our autonomous vehicles. You will play a key role in refining our data-driven methods to enable smooth and scalable unmanned operations in dense urban environments. Responsibilities Design, implement, and maintain high-performance, production-grade C++ software for trajectory candidate generation and scoring. Apply probabilistic , statistical , and machine learning methods to assess dynamic trajectory risk, calculate maneuver probabilities, and perform trajectory evaluation under uncertainty. Profile, optimize, and test online planning software to ensure low-latency execution and memory efficiency on embedded production platforms. Collaborate cross-functionally with Perception, Prediction, Motion Control, and Safety teams to refine scenario evaluation metrics and validate closed-loop planning behavior. Qualifications Bachelor’s or higher degree
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