About the role
In this Role, You Will...
- Use coding and analytical skills to derive insights that inform decisions and automation
- Test new frameworks and algorithms to improve model performance
- Work on a project such as drift monitoring, automated model retraining, predictive modeling, similarity search or statistical testing
Internship Requirements
- Currently working towards a B.S., M.S., Ph.D., or advanced degree in data science, statistics, computer science, or a related engineering program
- Good academic standing
- Expected to graduate between Fall 2027 and Summer 2028, and enrolled at an accredited college or university throughout the internship
- Able to commit to a full-time, 12-week internship beginning in May/June 2027
- Ability to relocate to the Bay Area, California, for the duration of the internship
- Interns at Zoox may not use any proprietary information they are working on as part of their thesis, any published work with their university, or to be distributed to anyone outside of Zoox
Qualifications
- Proficiency in Python and SQL
- Coursework in machine learning, computer vision, or NLP
- Familiarity with software-engineering practices such as Git, unit testing, debugging, and code reviews
- Understanding of supervised learning, model evaluation, and common sources of dataset bias and label noise.
- Experience with data analysis and visualization
- Ability to communicate data clearly to different audiences
Bonus Qualifications
- Experience with Databricks, Spark or cloud data tools
- Experience with dashboarding or experiment design
- Experience working with LLMs
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.