Motion Planning Engineer (PhD, Intern/Full-time)
About The Position
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. We are seeking a motivated PhD graduate with a strong research background in motion planning, robotics, or autonomous systems. In this role, you will apply your expertise in algorithm design and system integration to help develop next-generation planning capabilities for autonomous vehicles.
Requirements
- Recently completed or soon-to-complete PhD in Robotics, Computer Science, Electrical Engineering, or a related field.
- Research or Internship experience in one or more of the following: Motion planning algorithms (optimization, sampling, graph/search-based methods) Behavioral planning and decision-making under uncertainty Trajectory optimization and control Multi-agent interaction modeling
- Proven research ability demonstrated by publications in top-tier conferences (e.g., RSS, ICRA, IROS, CVPR, NeurIPS, CoRL).
- Hands-on experience in C++ and Python for implementing complex, real-time algorithms.
- Excellent analytical and communication skills, with a collaborative mindset.
Responsibilities
- Implement novel solutions for Behavioral Planning, enabling high-level decision-making for lane changes, merges, yields, and multi-agent interactions.
- Design and optimize motion planning algorithms that integrate geometry-based path reasoning and context-aware speed reasoning into smooth, safe trajectories.
- Develop and improve core geometry and velocity planning systems to ensure feasibility, compliance, and comfort across diverse driving scenarios.
- Model complex driving environments and agent behaviors to create a robust world representation for planning under uncertainty.
- Formulate cost functions and optimization frameworks that balance safety, comfort, and efficiency in trajectory selection.
- Analyze, test, and debug system performance through simulation and real-world data, conducting root-cause investigations and proposing enhancements.
- Collaborate with researchers and engineers across Perception, Prediction, and Control to ensure an integrated, reliable autonomy stack.
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What This Job Offers
Job Type
Full-time
Career Level
Intern
Education Level
Ph.D. or professional degree
Number of Employees
5,001-10,000 employees
