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Autonomous Systems Intern

🚀 Off-cycle Internship

Santa Clara

AI generated summary

  • You must be pursuing a Master’s or PhD in engineering or related field with a GPA of 3.0+. Strong coding ability in C++ or Python, organization skills, self-motivated, and excellent communication skills.
  • You will develop cutting-edge algorithms for autonomous systems, test them in real-world vehicles, and collaborate with other researchers in the field.

Off-cycle Internship

Software Engineering, EngineeringSanta Clara


  • We’re currently looking for an Autonomous Systems Research Intern to join our team in June 2024. On our team, we focus on scene understanding, road user prediction, trajectory planning, and autonomous vehicle simulation.  Our prediction and planning system is required to produce human-like behavior in challenging real-world scenes including decisive high-speed highway driving to nuanced low-speed city scenes with pedestrians, cyclists, and other vehicles.  As a member of the Autonomous Systems team, you will get to research and develop prediction and planning algorithms that scale from simple scenes to complex intersection thereby ensuring predictable, safe, and human-like autonomous vehicle behavior.


  • Currently be pursuing a Master’s or PhD degree in Mechanical, Electrical, or Software Engineering, Computer Science, Applied Mathematics, or a related field
  • GPA of 3.0 or above
  • Strong C++ or python coding ability
  • Strong organization ability with keen attention to detail
  • Be self-motivated and willing to challenge yourself to develop your talents and skillsets
  • Strong communication skills both written and oral

Education requirements

Currently Studying

Area of Responsibilities

Software Engineering


  • During the course of your internship, you will have the chance to:
  • Develop state-of-the-art perception, prediction or planning algorithms
  • Be responsible for testing your algorithms in simulation and real-world vehicles
  • Engage with other Autonomous Vehicle researchers


Work type

Full time

Work mode



Santa Clara