Motion Planning Engineer (PhD, Intern)

DiDi

  • San Jose, California
  • 30+ days ago
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    Skills

    • Algorithmsunmatched
    • Analysis Skillsunmatched
    • Autonomous Driving Systemsunmatched
    • C++ Programming Languageunmatched
    • Communication Skillsunmatched
    • Computer Scienceunmatched
    • Cost Controlunmatched
    • Debugging Skillsunmatched
    • Ecosystemsunmatched
    • Electrical Engineeringunmatched
    • Geometryunmatched
    • Graph Searchunmatched
    • Maintain Complianceunmatched
    • Optimization Algorithmunmatched
    • Research & Development (R&D)unmatched
    • Roboticsunmatched
    • Root Cause Analysisunmatched
    • Simulationunmatched
    • System Integration (SI)unmatched
    • Team Playerunmatched
    • Vehicle Fleetsunmatched

    Description

    About The Company

    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.


    About The Role

    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.

    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.

    Qualifications

    • 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++ for implementing complex, real-time algorithms.
    • Excellent analytical and communication skills, with a collaborative mindset.
    • For Internship Applicants: This role offers a clear pathway, with top-performing interns receiving the opportunity to convert to a full-time engineer upon successful completion of the program.

    The hourly rate for the Intern position in the selected city is $46. Interns will also be eligible for Intern benefits. 

    Applications are accepted on an ongoing basis. This posting is for an existing vacancy.

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    Numbers & Facts

    LocationSan Jose, California
    Websitev.didi.cn/AQnxlBa

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