Program Tooling & Analytics Developer

Build and ship internal web tools and dashboards on top of existing Zoox data sources, owning them from prototype through the version leaders rely on.

Start from the decision, not the dataset — work with TPMs and engineering partners to turn a vague business question into a scoped tool, then iterate quickly on their feedback.

Design the data layer behind those tools — queries, transformations, and pipelines that keep them current without manual intervention.

Extend our geospatial tooling with new attributes, data sources, and visualizations that sharpen where and how we expand service.

Handle real-world data honestly — surface coverage gaps, flag low-confidence results, and make each tool's assumptions visible to the people acting on its output.

Time commitment is flexible and agreed at offer — part-time alongside coursework or full-time both work. A minimum of six months is preferred for continuity.

Must adhere to Zoox confidentiality requirements, including refraining from using or sharing proprietary company information outside of Zoox, such as in academic research, theses, publications, or presentations.

Currently enrolled in, or recently completed, a BS or MS in Computer Science, Data Science, Engineering, or a related quantitative field — or equivalent practical experience.

Demonstrated experience building web tools or dashboards end to end, through coursework, internships, research, or personal projects. We care more about what you have shipped than where.

Strong Python and working SQL, plus enough front-end capability to build a usable interface — React/TypeScript or a Python framework such as Streamlit, Dash, or Flask — with a visualization library (Plotly, D3, or similar).

Effective use of generative AI coding tools such as Claude Code, with the discipline to review and test what they produce rather than ship it unread.

Solid software fundamentals — version control, code review, readable code — and comfort with messy, incomplete real-world data, including the judgment to know when a result is not trustworthy enough to present.

Clear written and verbal communication, and the ability to explain a technical result to a non-specialist audience.

Familiarity with geospatial tooling — GeoPandas, Shapely, OSMnx, H3 or equivalent spatial indexing, and map rendering with deck.gl, Mapbox, Leaflet, or QGIS — and with open data sources such as OpenStreetMap or Overture Maps.

Experience with large-scale data processing and BI tooling — Databricks, PySpark, Tableau, or Looker.

Background in routing, graph algorithms, or network analysis.

Exposure to autonomous vehicles, robotics, mapping, or mobility data, or prior experience building an internal tool other people depended on.