ML Engineer, Motion Planning

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ML Engineer, Motion Planning in USA new

DiDi
Full time
Email

USA
10/19/2026
Report

San Jose, CA

Responsibilities

Develop ML-powered systems for driving behavior decisions, scene understanding, and trajectory generation for Level 4 autonomous driving.
Frame real-world driving challenges as ML problems and apply imitation learning, reinforcement learning, learned planners, and end-to-end models.
Design algorithms that improve the safety, generalization, and reliability of autonomous-driving planning.
Integrate ML-driven approaches with rule-based and optimization-based planning systems.
Develop and evaluate large-scale ML models using real-world driving data and simulation, then integrate them into production systems under compute, latency, and safety constraints.
Build scenario-mining, training, evaluation, closed-loop simulation, and real-world-performance measurement infrastructure.
Collaborate with ML Infrastructure, Perception, Prediction, Planning, Mapping, and Systems Engineering teams to investigate failures and deploy improvements.

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.
3+ years of industry experience for Senior or 5+ years for Staff in machine learning, autonomous driving, robotics, or related fields.
Track record of developing and deploying production ML models, ideally for real-time, embedded, or autonomous-driving systems.
Strong proficiency in Python and C++ and hands-on experience with modern deep learning frameworks such as PyTorch, JAX, or TensorFlow.
Deep understanding of deep learning techniques including transformers, BEV and 3D representations, and trajectory prediction or generation 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 ML to motion planning, robotics, or related decision-making problems and integrating learning-based methods with traditional planning.
Ability to own ambiguous technical challenges end to end while balancing research innovation, production constraints, safety, reliability, and engineering trade-offs.
Demonstrated technical impact through research publications, open-source contributions, or significant industry achievements.
Preferred: experience with closed-loop simulation, large-scale evaluation, road-structure representations, end-to-end autonomous-driving models, vision-language or foundation models, and real-time onboard inference optimization.
Preferred: experience with TensorRT, CUDA, quantization, mentoring junior engineers, supervising research interns, or leading small technical teams.
Preferred: publications at conferences such as CVPR, ICCV, ECCV, NeurIPS, CoRL, ICRA, IROS, or IV, or equivalent industry impact.

Benefits

Base salary is listed separately for Senior and Staff positions; compensation also includes bonus, equity, and benefits.
Compensation varies by role, level, location, skills, experience, and education or training.

Salary: $170k - $339k/yr

DiDi

DiDi Global Inc. is a leading mobility technology platform. It offers a wide range of app-based services across Asia Pacific, Latin America, and other global markets, including ride hailing, taxi hailing, designated driving, hitch and other forms of shared mobility as well as certain energy and v...

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