ML Engineer, Motion Planning

About Didi Autonomous Driving Founded in 2016, DiDi's autonomous driving unit is dedicated to developing Level 4 autonomous driving technology to make transportation safer, more efficient, and accessible. In 2019, the unit became an independent company, Voyager, to accelerate technology development, commercialization, and large-scale deployment. By combining DiDi's unique data advantage, operational expertise, and mobility ecosystem, we aim to build a reliable, scalable autonomous fleet that integrates seamlessly with human-driven services. Driven by real-world customer needs and a strong commitment to safety and user experience , our mission is to deliver autonomous mobility solutions that create meaningful value for riders and communities. About The Role We are looking for an experienced and driven Machine Learning Engineer to join our autonomous vehicle team and develop the next-generation ML-powered planning system for Voyager's autonomous driving platform. You will leverage frontier ML techniques, including learned planners and end-to-end models, to build intelligent systems that understand complex driving environments, make data-driven decisions, and navigate safely and efficiently. Working alongside leading industry experts, you will leverage DiDi's unique data advantage , including petabytes of real-world driving data, to advance model performance, build robust evaluation frameworks that correlate with on-road safety, and accelerate the deployment and scaling of our driverless technology. Responsibilities Develop next-generation ML-powered systems that generate driving behavior decisions, scene understanding, and trajectories to support our L4 autonomous driving deployment roadmap. Frame open-ended, real-world driving challenges as well-defined ML problems; research and apply cutting-edge techniques, including imitation learning, reinforcement learning, learned planners, and end-to-end models. Design data-driven algorithms that improve the safety, generalization, and reliability of autonomous driving, including robust scene understanding and planning in complex or changing road environments. Develop hybrid planning solutions that integrate ML-driven approaches with traditional rule-based and optimization-based planning , leveraging their complementary strengths to achieve superior safety, efficiency, and robustness in complex driving scenarios. Develop and evaluate large-scale ML models using real-world driving data and simulation, translating learned representations into actionable planning guidance and integrating models into production systems under onboard compute, latency, and safety constraints. Build infrastructure for rapid iteration, including challenging scenario mining, training and evaluation pipelines, closed-loop simulation, and metrics that correlate with real-world driving performance. Collaborate closely with ML Infrastructure, Perception, Prediction, Planning, Mapping, and Systems Engineering teams to investigate failures, drive end-to-end improvements, and deploy solutions into real-world autonomous driving. Qualifications B.S., M.S., or Ph.D. in Computer Science, Machine Learning, Robotics, Electrical Engineering, Mechanical Engineering, or a related technical field, or equivalent practical experience. 3+ years of industry experience for Senior, or 5+ years for Staff, in machine learning, autonomous driving, robotics, or related fields. Proven track record of developing and deploying ML models in production, ideally for real-time, embedded, or autonomous driving systems. Strong proficiency in Python and C++, with experience developing robust, efficient production software and hands-on experience with modern deep learning frameworks (e.g., PyTorch, JAX, TensorFlow). Deep understanding of modern deep learning techniques, including transformers, BEV and 3D representations, a