Responsibilities

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

Requirements

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
Available for a 6 month project
Able to commit to at least 40 hours per week
Ability to commute on-site to Foster City

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