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, focusing on behavioral planning, trajectory optimization, and system integration with perception and control modules.
What you'll do
- Implement behavioral planning solutions for lane changes, merges, yields, and multi-agent interactions
- Design and optimize motion planning algorithms combining geometry-based and velocity-based reasoning
- Develop geometry and velocity planning systems ensuring feasibility, compliance, and comfort
- Model driving environments and agent behaviors for robust planning under uncertainty
- Formulate cost functions and optimization frameworks balancing safety, comfort, and efficiency
- Analyze system performance via simulation and real-world data 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 of real-time algorithms
- Robotics and autonomous systems
- Mathematical optimization and cost function design
- Simulation and data analysis
Benefits
- Pathway to full-time conversion for top-performing interns
- Work on Level 4 autonomous driving technology
- Collaborative environment across Perception, Prediction, and Control teams
- Intern benefits package
- Ongoing applications accepted for existing vacancy
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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
- Describe a motion planning algorithm you developed during your PhD and how you validated its performance.
- How have you integrated trajectory optimization with behavioral decision-making in autonomous systems?