DiDi Labs
Motion Planning Engineer (PhD, Intern)
About this role
DiDi Autonomous Driving seeks a PhD graduate to develop motion planning algorithms for Level 4 autonomous vehicles. You'll design behavioral planning solutions, optimize trajectory generation, and collaborate across the autonomy stack to enable safe, efficient autonomous driving.
What you'll do
- Implement behavioral planning algorithms for lane changes, merges, yields, and multi-agent interactions
- Design and optimize motion planning algorithms integrating geometry-based path and velocity reasoning
- Develop geometry and velocity planning systems for diverse driving scenarios
- Model driving environments and agent behaviors for robust world representation under uncertainty
- Formulate cost functions and optimization frameworks balancing safety, comfort, and efficiency
- Test system performance through simulation and real-world data analysis with root-cause investigation
What they're looking for
- Motion planning algorithms (optimization, sampling, graph/search methods)
- Behavioral planning and decision-making under uncertainty
- Trajectory optimization and control
- Multi-agent interaction modeling
- C++ implementation for real-time algorithms
- Robotics and autonomous systems
- Algorithm design and system integration
- Analytical and communication skills
Benefits
- Internship benefits package
- Pathway to full-time conversion for top performers
- Work on Level 4 autonomous driving technology
- Collaboration with world-class autonomy research team
- Experience with real-world autonomous vehicle deployment
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DiDi Labs
DiDi Labs develops autonomous driving technology for Level 4 vehicles, focusing on motion planning, behavioral decision-making, map fusion, and robotic simulation systems. The company is hiring software engineers and researchers to build algorithms, trajectory optimization systems, and cloud-based simulation infrastructure that enable safe and intelligent autonomous vehicle navigation.
View all jobs at DiDi LabsLikely interview questions
- Walk us through a motion planning algorithm you developed during your PhD research and how it handles multi-agent interactions.
- How do you approach balancing safety, comfort, and efficiency when formulating trajectory optimization objectives?