About The Position

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.

Requirements

  • 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

Nice To Haves

  • 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

Responsibilities

  • 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

Benefits

  • Compensation for this role is $30/hour.
  • Benefits eligibility is determined by the vendor.
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