Contract Student Worker - Autonomy Safety Data Engineer

About Zoox

Zoox is an autonomous ridehailing company building the world's first purpose-built robotaxi — fully electric, bidirectional, with no steering wheel or driver's seat. Backed by Amazon and founded to make transportation safer, cleaner, and more accessible, Zoox designs its vehicles entirely around the rider. We're currently operating in Las Vegas and San Francisco, with Austin and Miami on the horizon, and testing underway across seven U.S. markets.

About Our Part-Time Student Worker Program

Zoox's part-time student worker program puts you at the center of one of the most ambitious challenges in transportation. You'll contribute to real projects, work alongside engineers and researchers pushing the boundaries of autonomous technology, and gain experience that goes well beyond the classroom. We're looking for students who bring strong academic foundations, curiosity that doesn't stop at coursework, and a drive to be part of something that matters.

This role focuses on building a data-driven safety risk model that quantifies and improves autonomous-vehicle driving performance, along with the data analysis work that supports it. The student will work within the Safety Strategy & Operations team on a 6-month project spanning model development, empirical experimentation, and large-scale driving/simulation data analysis.

Support designing, building, and iterating on a data-driven safety risk model that quantifies driving performance and surfaces safety-relevant signals across the autonomy stack

Assist developing and maintaining dataset management pipelines — curation, labeling, versioning, and quality checks — that feed the risk model and downstream analyses

Support defining and running empirical experiments that “show it with data” rather than relying on assumptions

Assist analyzing large-scale driving and simulation datasets to identify trends, edge cases, and opportunities to improve autonomy performance

Currently pursuing a B.S. or M.S. in a relevant quantitative field (Engineering, Computer Science, Statistics, Physics, or similar)

Strong programming skills in Python

Help work through ambiguous, open-ended problems with a researcher’s mindset and a bias toward rapid iteration

Solid data manipulation understanding (e.g., SQL, PySpark, Scala)

Solid foundation in statistics, machine learning, and risk or reliability modeling

Help communicate complex results, trade-offs, and uncertainty clearly to the team and stakeholders

Comfort operating independently under high uncertainty on open-ended problems

Excellent written and verbal communication; able to convey complexity and ambiguity clearly

Experience with safety, reliability, or risk modeling (e.g., survival analysis, Bayesian methods, causal inference)

Experience with large-scale dataset management, data pipelines, or MLOps tooling

Strong teamwork and collaboration skills

Background in autonomous vehicles, robotics, or a related quantitative discipline

Prior research experience taking ambiguous, end-to-end problems from zero to a result, independently

Genuine interest in autonomous vehicles and Zoox’s mission

Currently enrolled in a B.S. or M.S. program in a relevant discipline.

Available to commit to a minimum three-month assignment.

Able to commit a minimum of 20 hours per week.

Able to work on-site at one of our office locations.

Must adhere with Zoox confidentiality requirements, including refraining from using or sharing proprietary company information outside of Zoox, such as in academic research, theses, publications, or presentations.