Software Engineer Intern

About Zoox

Zoox is developing the first ground-up, fully autonomous vehicle fleet and the supporting ecosystem required to bring this technology to market. Sitting at the intersection of robotics, machine learning, and design, Zoox aims to provide the next generation of mobility-as-a-service in urban environments.

About Our Internship Program

Zoox's internship program offers hands-on experience with cutting-edge technology, mentorship, and the opportunity to make meaningful contributions to real projects. We seek interns who demonstrate strong academic performance, engagement beyond the classroom, intellectual curiosity, and a genuine interest in Zoox's mission.

Job Description: Autonomy Software Intern [Summer 2027]

Autonomy Software builds the software that lets the Zoox robotaxi perceive its surroundings, predict what others will do, and plan and control the vehicle with no one behind the wheel. Interns join one of the teams below, work with a mentor on an open research or engineering question, and see a direct path from their work to the system that runs on Zoox vehicles and in simulation.

Interns in this pipeline may be considered for opportunities across a range of teams, including:

• Autonomy Behaviors: Decision-making, planning & control modules. Projects include machine-learned planning, long-horizon reasoning, configurable driving styles, freeway driving, and new vehicle platform bring-up.
• Perception: Understanding surroundings using camera, lidar, radar, and audio. Projects include object detection and tracking, multimodal foundation models, scene understanding, occupancy, and 3D mapping.
• Guidance & Routing: Route-level planning and offline mapping, including the mission planner, map data delivery, and road network tools.
• Collision Avoidance System: Independent safety layer with its own perception and planning. This team also works on evaluation and data tooling for verification.

In this Role, You Will...

• Work on research or advanced engineering problems with the potential to revolutionize the existing Autonomy Software stack.
• Design, build, and evaluate state-of-the-art algorithms or machine learning models for a real autonomous driving system.
• Train and test models on large-scale driving and simulation data, and analyze failure modes to measure improvements.
• Write production-quality C++ and Python.
• Present results to your team and at the end-of-summer project showcase, and contribute to publishable research where the project allows.

Requirements

• Currently working towards a M.S. or Ph.D. in computer science, robotics, electrical engineering, or a related engineering program.
• Expected to graduate between Fall 2027 and Summer 2028, and enrolled at an accredited college or university throughout the internship.
• Able to commit to a full-time, 12-week internship beginning in May/June 2027.
• Ability to relocate to the Bay Area, California, for the duration of the internship.
• Interns may not use any proprietary information they are working on as part of their thesis, any published work with their university, or to be distributed to anyone outside of Zoox.

Qualifications

• Currently working towards a M.S. or Ph.D. in computer science, robotics, electrical engineering, or a related engineering program.
• Strong programming skills in C++ and/or Python.
• Solid computer science fundamentals, including data structures and algorithms.
• Experience with PyTorch, Jax, Tensorflow, Numpy.
• Experience in at least one of machine learning, computer vision, motion planning, controls, prediction, or mapping.
• Clear communication of technical results.

Bonus Qualifications

• Publications in top ML or robotics conferences (e.g. NeurIPS, CVPR, ICRA).
• Experience with autonomous driving or learned robotic planning.
• Experience with large-scale, multi-node training workloads.

Compensation

• Monthly salary range: $6,500 to $10,500.
• Compensation will vary based on geographic location and level of education.
• Additional benefits may include medical insurance and a relocation stipend.