Software Engineer – Map Fusion & Planning

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 Software Engineer / Senior Software Engineer to develop the next-generation map fusion and motion planning systems for our autonomous vehicles. In this role, you will bridge the gap between semantic HD maps, real-time sensor perception, and vehicle trajectory generation. You will design scalable software infrastructure, implement advanced geometric and deep learning frameworks, and develop the planning algorithms that enable our vehicles to navigate complex, dynamic environments safely and predictably.

Responsibilities

System Architecture : Architect the data flow pipelines and APIs for map fusion, real-time map vectorization, and behavior/motion planning modules. Algorithm Deployment : Design and deploy robust software frameworks that integrate offline High-Definition (HD) maps with online perception data to create a unified local environment model. Advanced Mapping Networks : Implement and optimize state-of-the-art networks utilizing DETR-style, query-based vector decoding in bird's-eye-view (BEV) for online map element generation.

Motion Planning Optimization : Design, implement, and validate core motion planning algorithms, establishing a tight feedback loop between vectorized map features, path generation, and trajectory optimization. Model Deployment Pipelines : Own the end-to-end deployment pipeline for deep learning mapping models—from Python-based training and ONNX optimization to highly efficient runtime execution in C++. Safety Anomaly Detection : Develop real-time map anomaly and scene-change detection algorithms to ensure planning system reliability under varying or outdated map conditions.

Performance Optimization : Optimize system latency, CPU/GPU memory footprint, and multi-threaded execution of safety-critical C++ modules.

Qualifications

S. D. in Computer Science, Robotics, Electrical Engineering, or a related field.

Experience: 3+ years (Software Engineer) / 5+ years (Senior Software Engineer) of experience in autonomous driving, robotics architecture, or spatial computing. Software Mastery: Expert proficiency in production-grade C++ (Modern C++14/17/20, multi-threading, memory management) and strong prototyping proficiency in Python. , A*, Dijkstra, Hybrid A*, sampling-based planners like RRT*) and kinematic/dynamic vehicle models.

Robotics Core: Deep understanding of robotics fundamentals, including coordinate transformations, spatial geometry, and state estimation. , ROS2, DDS) and distributed software architectures.

Preferred Qualifications

, OSQP, Ipopt, Ceres Solver). , MapTR, VectorNet) that leverage query-based BEV perception. Deep Learning Runtime Deployment: Proven track record of exporting complex deep learning architectures via ONNX and deploying them into real-time C++ production environments using TensorRT.