Job Description

About Zoox

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.

About Our Part Time Student Worker Program

Zoox’s 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 part time student workers  who demonstrate strong academic performance, engagement beyond the classroom, intellectual curiosity, and a genuine interest in Zoox’s mission. 

Project Overview

Zoox is seeking a motivated (graduate or upper-level undergraduate) student worker to join our team, focusing on (building and validating a driving risk assessment system, an experimental RAG system for proactively testing autonomous-vehicle behavior)

In this role, you will:

• Design, build, and iterate on an end-to-end RAG pipeline for autonomy validation, owning it from prototype to demo
• Define and run empirical experiments that "show it with data" rather than relying on assumptions
• Work through ambiguous, open-ended problems with a researcher's mindset and a bias toward rapid iteration
• Communicate complex results, trade-offs, and uncertainty clearly to the team and stakeholders

Qualifications

• Currently pursuing a B.S. or M.S. in a relevant quantitative field (Engineering, CS, Physics, Neuroscience, Biology/Computational Bio, or similar)
• Strong programming skills in Python
• Solid data manipulation understanding (e.g., SQL, Pyspark, Scala)
• Solid foundation in machine learning and statistics
• Comfort operating independently under high uncertainty on open-ended problems
• Excellent written and verbal communication; able to convey complexity and ambiguity clearly
• Strong teamwork and collaboration skills

Bonus Qualifications

• Experience with RAG systems, LLMs, or vision-language models (VLMs)
• Background in a quantitative discipline (Neuroscience, Physics, Computational Bio, etc.) 
• Prior research experience taking ambiguous, end-to-end problems from zero to a result, independently
• Genuine interest in autonomous vehicles and Zoox's mission

Program Requirements

• Currently pursuing a B.S. or M.S., in a relevant engineering field.
• Available for a 6 month project
• Able to commit to at least 40 hours per week
• Ability to commute on-site to Foster City
• Student Worker may not use proprietary Zoox information in university theses, publications, or share it outside.

$30 per hour.

This is a contract position and employment for this position will be through a vendor contracted with Zoox. The hourly pay range is posted and you will be eligible for a benefits package as offered by the vendor.

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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