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Salary
≈ $207k – $454k per year (Estimated)
Location
Hybrid (Foster City, United States)
Seniority
Intern
Visa
H-1B filings in 12 months: 373 · for this role: 169 · green card filings: 120
Employment
Full-Time

Confirmed on the employer's own hiring board on Oct 17, 2026. First seen by Alion on Aug 7, 2026. Zoox scores B on the Alion truth index.

Overview
Company
Impact
Profile match
Zoox is an American autonomous vehicle company founded in 2014 that designs a purpose-built robotaxi rather than retrofitting an existing car. Its vehicle has no steering wheel or driver position, seats four passengers facing each other, drives equally well in either direction and was developed alongside the sensors, compute and software that operate it, which required the company to certify the design under its own self-certification of federal motor vehicle safety standards. Amazon acquired the company in 2020 and it has since begun public robotaxi service in Las Vegas and San Francisco while testing in several other American cities.

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.

Role Overview

Support Zoox's Rules of the Road (RotR) program, where a miner + LLM/VLM tooling method automatically mines and triages potential road-rule violation events from simulation and fleet data. The student will be helping create or improve the data miners that generate the candidate events, help develop and evaluate the VLM triagers - curating golden datasets, measuring precision/recall against expert triage, and extending automation to new regulations and data sources.

Responsibilities

  • Develop and iterate data miners locating potential RotR Violations following SSO requirements
  • Develop and iterate on VLM/LLM workflows and pipelines for classifying RotR events
  • Curate golden datasets and evaluate triager precision/recall against human triage

Required Qualifications

  • Extend triagers from simulation to fleet/service data
  • Strong Python / Pyspark / SQL skills
  • Coursework in machine learning, computer vision, or NLP. Understanding of supervised learning, model evaluation, and common sources of dataset bias and label noise.
  • Analyze error cases and translate findings into pipeline improvements
  • Hands-on experience building production LLM/VLM applications, including agentic workflows, RAG, tool calling, evaluation, and/or fine-tuning.
  • Familiarity with software-engineering practices such as Git, unit testing, debugging, and code reviews.

Bonus Qualifications

  • Strong Scala skills.
  • Experience with Spark optimization and distributed data-processing systems.
  • Experience with autonomous vehicles, robotics, mapping, or transportation-related datasets.

Program Requirements

  • Experience evaluating multimodal or vision-language models.
  • Currently enrolled in a B.S. or M.S. program in a relevant discipline.
  • Experience with prompt optimization, model fine-tuning, or human-in-the-loop ML systems.
  • Available to commit to a minimum three-month assignment.
  • Able to commit a minimum of 40 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.
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