Phd Research Intern, Physical AI In Perception

Zoox Internship Program

Zoox is transforming mobility with fully autonomous, electric vehicles designed from the ground up for a driverless future. Our mission is to make transportation safer, more sustainable, and accessible to everyone. At Zoox, innovation, collaboration, and a bold vision for the future drive everything we do.

Internship Program Overview

Zoox's internship program offers hands-on experience with cutting-edge technology, mentorship from some of the industry's brightest minds, 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.

Project Overview

This internship is part of the Perception Semantics team, focused on advancing on-robot AI systems that enable machines to understand and interact with the physical AI world. You'll work on cutting-edge problems in vision-language-action (VLA) modeling, world modeling, spatial reasoning, and mapping, contributing to both research and real-world deployment.

Projects are open-ended and research-driven, giving you the opportunity to explore new ideas, develop novel approaches, and evaluate them in realistic settings. This role is ideal for Ph.D. students interested in pushing the boundaries of computer vision and embodied AI while seeing their work translate into real-world impact.

Qualifications

• Currently enrolled in the Ph.D program in Computer Science, Electrical/Computer Engineering, or related field, with the specialization in the CV/NLP/ML
• Experience in multi-modal modeling (vision, language, or planning), with deep understanding of Vision Language Model, vision foundation model, flow-matching, temporal modeling, and reinforcement learning techniques
• Strong proficiency in PyTorch and modern transformer-based model design

Bonus Qualifications

• Publication records in top-tier AI conferences (CVPR, ICCV, ECCV, NeurIPS, ICLR, ICML, etc)
• Prior experience building foundation or end-to-end driving models for autonomous driving or robotics, or working deeply on LLM/VLM architectures (e.g., ViT, Flamingo, BEVFormer, RT-2, or GRPO-style policies)
• Knowledge of RLHF/DPO/GRPO, trajectory prediction for safety

Requirements

• Currently working towards a Ph.D in a relevant engineering program
• Good academic standing
• Able to commit to a 12-week internship during one of the following summer 2026 cohorts: May 18th - August 7th, OR May 26th - August 14th, OR June 15th - September 4th
• 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, Massachusetts) 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

Compensation

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

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.

If you need an accommodation to participate in the application or interview process please reach out to accommodations@zoox.com 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.