Listing unavailableEngineeringHybrid

Contract Student Worker Automation & Behavioral Science Engineer

Zoox · Hybrid

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Last seen by MeritLog September 12, 2026Source: LeverSource version: lever-postings-v1

Source: the employer's Lever job board. Open the original listing for current details. Availability is not verified for this retained page.

Job details

Work model: Hybrid
Salary: Not listed by source
Location: Foster City, CA

What the role asks for

What you'd do

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

What they're asking for

Currently pursuing a B.S. or M.S. in a relevant quantitative field (Engineering, CS, Physics, Neuroscience, Biology/Computational Bio, or similar)Education
Strong programming skills in PythonSkill
Solid data manipulation understanding (e.g., SQL, Pyspark, Scala)Skill
Solid foundation in machine learning and statisticsSkill
Comfort operating independently under high uncertainty on open-ended problemsSkill
Excellent written and verbal communication; able to convey complexity and ambiguity clearlySkill
Strong teamwork and collaboration skillsSkill
Experience with RAG systems, LLMs, or vision-language models (VLMs)SkillPreferred
Background in a quantitative discipline (Neuroscience, Physics, Computational Bio, etc.)SkillPreferred
Prior research experience taking ambiguous, end-to-end problems from zero to a result, independentlySkillPreferred
Genuine interest in autonomous vehicles and Zoox's missionSkillPreferred
Currently pursuing a B.S. or M.S., in a relevant engineering field.EducationPreferred
Available for a 6 month projectSkillPreferred
Able to commit to at least 20 hours per weekSkillPreferred
Ability to commute on-site to Foster CitySkillPreferred
Student Worker may not use proprietary Zoox information in university theses, publications, or share it outside.SkillPreferred

Parsed by MeritLog from the employer’s own posting. The full description follows below.

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 20 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. Follow us on LinkedIn Accommodations 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.

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