ML Engineer, Motion Planning

DiDi•San Jose, CA

5d•$169,783 - $338,694

About The Position

DiDi Autonomous Driving, founded in 2016 and spun off as the independent company Voyager in 2019, develops Level 4 autonomous driving technology. The team aims to build reliable, scalable autonomous mobility solutions using DiDi’s real-world data, operational expertise, and mobility ecosystem. The Machine Learning Engineer will develop next-generation ML-powered planning systems for Voyager’s autonomous driving platform. The role applies techniques such as learned planners and end-to-end models to interpret driving environments, make data-driven decisions, and navigate safely and efficiently. It also involves using real-world driving data to improve model performance, building evaluation frameworks correlated with on-road safety, and supporting deployment and scaling of driverless technology.

Requirements

Bachelor’s, master’s, or Ph.D. in Computer Science, Machine Learning, Robotics, Electrical Engineering, Mechanical Engineering, or a related technical field, or equivalent practical experience.
At least 3 years of industry experience for Senior level, or at least 5 years for Staff level, in machine learning, autonomous driving, robotics, or related fields.
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.
Hands-on experience with modern deep learning frameworks such as PyTorch, JAX, or TensorFlow.
Understanding of modern deep learning techniques, including transformers, BEV and 3D representations, and trajectory prediction or generation models; hands-on experience training, evaluating, and debugging models.
Foundations in linear algebra, control theory, or motion planning, including path and trajectory generation, trajectory evaluation, cost function design, and lane and road geometry.
Experience applying machine learning to motion planning, robotics, or related decision-making problems, and integrating learning-based approaches with traditional planning methods.
Ability to own ambiguous technical challenges end to end while balancing research innovation with production constraints, safety, reliability, and engineering trade-offs.
Demonstrated technical impact through research publications, open-source contributions, or significant industry achievements.

Nice To Haves

Engineering experience applying ML-driven approaches to complex real-world problems and translating research ideas into robust, production-quality systems.
Experience integrating learning-based approaches with traditional rule-based or optimization-based planning systems.
Experience with closed-loop simulation, large-scale evaluation, and data-driven model iteration for autonomous driving.
Experience mentoring junior engineers, supervising research interns, or leading small technical teams.
Experience with road-structure representations, including lane graphs, topology reasoning, scene understanding, or uncertainty estimation in complex driving environments.
Experience with end-to-end autonomous driving models, learned planners, vision-language models, or foundation models applied to driving and robotics.
Experience deploying and optimizing ML models for real-time onboard inference, including TensorRT, CUDA, quantization, and compute or latency optimization.
Publications at leading conferences such as CVPR, ICCV, ECCV, NeurIPS, CoRL, ICRA, IROS, or IV, or equivalent demonstrated technical impact in industry.

Responsibilities

Develop ML-powered systems for driving behavior decisions, scene understanding, and trajectory generation to support the L4 autonomous driving roadmap.
Frame real-world driving challenges as ML problems and apply techniques including imitation learning, reinforcement learning, learned planners, and end-to-end models.
Design data-driven algorithms to improve safety, generalization, and reliability, including scene understanding and planning in complex or changing road environments.
Develop hybrid planning solutions that combine ML-driven approaches with traditional rule-based and optimization-based planning.
Develop and evaluate large-scale ML models using real-world driving data and simulation, and integrate them into production systems subject to onboard compute, latency, and safety constraints.
Build infrastructure for rapid iteration, including challenging-scenario mining, training and evaluation pipelines, closed-loop simulation, and metrics correlated with real-world driving performance.
Collaborate with ML Infrastructure, Perception, Prediction, Planning, Mapping, and Systems Engineering teams to investigate failures, improve systems end to end, and deploy solutions in real-world autonomous driving.