Job Description

Zoox’s internship program provides hands-on experiences with state of the art technology, mentorship from some of the industry's brightest minds, and the opportunity to play a part in our success. Internships at Zoox are reserved for those who demonstrate outstanding academic performance, activities outside their course work, aptitude, curiosity, and a passion for Zoox's mission.

High-definition semantic maps are critical for enabling fully autonomous driving. We are looking for highly motivated individuals who are passionate about machine learning and computer vision. You will be part of our mapping team with a diverse group of researchers and engineers, working together to build core machine perception algorithms to understand the natural environments and build semantic maps in an automated way.

In this role, you will:

• Design and develop novel algorithms and ML models for 2D/3D machine perception in real-world environments
• Stay up-to-date with the latest state-of-the-art research in computer vision and machine learning, and apply learned knowledge to improve our systems
• Coordinate cross-functional initiatives and collaborate with engineers from Mapping, Perception, Data Science, and more

Requirements:

• Currently working towards a B.S., M.S., Ph.D., or advanced degree in a relevant engineering program
• Must be returning to school to continue your education upon completing this internship
• Good academic standing
• Able to commit to a 12-week internship beginning in May or June of 2026.
• At least one previous industry internship, co-op, or project completed in a relevant area
• Ability to relocate to the Bay Area, California or Boston for the duration of the internship
• Interns at Zoox 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:

• Experience with training and deploying deep learning models
• Experience with 3D computer vision and projective geometry
• Proficiency in Python and/or C++
• Experience with deep learning frameworks such as PyTorch

Bonus Qualifications:

• Conference or journal publications in Computer Vision, Machine Learning or Robotics related venues (CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, ICRA, RSS, etc.)
• Experience with high-definition (HD) maps
• Experience with object detection, object recognition, semantic segmentation and/or semantic scene understanding
• Experience with 3D reconstruction, SLAM and/or Structure from Motion (SFM)
• Experience with Geographic Information System (GIS)

Compensation:

The monthly salary range for this position is $5,500 to $9,500. Compensation will vary based on geographic location and level of education. Additional benefits may include medical insurance, and a housing stipend (relocation assistance will be offered based on eligibility).

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. We’re looking for top talent that shares our passion and wants to be part of a fast-moving and highly execution-oriented team.

Accommodations

If you need an accommodation to participate in the application or interview process please reach out to [email protected] or your assigned recruiter.

A Final Note:

You do not need to match every listed expectation to apply for this position. Here at Zoox, we know that diverse perspectives foster the innovation we need to be successful, and we are committed to building a team that encompasses a variety of backgrounds, experiences, and skills.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

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