PhD Intern: Autonomous Driving ML & Robotics

Weride; San Jose (CA); On-site; USD 34,440 - 55,104; Part time

Job summary

WeRide offers an internship program where PhD students will contribute to a range of autonomous driving projects across Perception, Prediction, Planning and Control, Mapping, and Infrastructure teams. You will develop and implement software and ML solutions for our next‑generation driving system in a collaborative, world‑class engineering environment.

Interns work on real deployments, analyzing simulation and road data to address motion planning and vehicle control challenges, with 1‑on‑1

Qualifications

• Currently pursuing a PhD with expected graduation before end of 2027.
• Proficient in C++ and Python with knowledge of latest features.
• Strong understanding of machine learning approaches (vision, NLP, multi‑modal models).
• Excellent analytical and problem‑solving abilities.
• Excellent communication and cross‑functional collaboration skills.

Responsibilities

• Join teams such as Perception, Prediction, Planning and Control, Mapping and Localization, Simulation, and Infrastructure.
• Contribute on software for next version of autonomous driving solution.
• Develop and implement machine learning solutions across projects.
• Analyze data from simulations and real‑world tests to improve motion planning and control.
• Collaborate with cross‑functional teams to integrate models into systems.

Skills

C++

Python

ML knowledge

Analytical skills

Communication

Education

PhD in Robotics, Computer Science, Machine Learning, or related field

Job description

WeRide offers an internship program where PhD students will contribute to a range of autonomous driving projects across Perception, Prediction, Planning and Control, Mapping, and Infrastructure teams. You will develop and implement software and ML solutions for our next‑generation driving system in a collaborative, world‑class engineering environment.

Interns work on real deployments, analyzing simulation and road data to address motion planning and vehicle control challenges, with 1‑on‑1