Data Engineer (SE / Sr SE)

Knowing how well our virtual driver drives — and why it fell short — is what lets us ship with confidence. In this role, you will own that loop end to end: the metrics that quantify driving performance, the pipelines that compute them at fleet scale, the analysis that turns them into judgments about autonomy behavior — including where on the map that behavior changes — the data and tooling that gate software releases, and the agentic workflows that take a detected issue from triage to a proposed fix. You will work primarily in Python across large-scale data processing, geospatial analytics, evaluation frameworks, and LLM-powered automation. We welcome engineers from data engineering, analytics, geospatial, evaluation, or robotics backgrounds; prior autonomous-vehicle experience is helpful but not required.

We are open to candidates at either the Engineer or Software Engineer level. Level will be determined by experience, technical depth, scope of ownership, and demonstrated impact. You do not need experience with every technology in our stack; we value strong fundamentals, ownership, and the ability to learn.

Responsibilities:

• Define and compute driving performance metrics — covering safety, comfort, progress, interventions, and compliance — and build the scalable pipelines that evaluate them consistently across fleet and simulation data

• Analyze road-test and simulation data in depth to identify trends, regressions, and anomalies in autonomy behavior, and turn them into clear findings that engineering teams act on

• Build geospatial analytics over fleet driving data: map-matched metrics, route and corridor performance, location-based clustering of events and issues, and geographic coverage analysis that shows where the virtual driver performs well and where it struggles

• Build the data foundations and tooling for release management, including release-over-release comparisons, readiness and gating criteria, and traceable evidence supporting release decisions

• Build AI agentic workflows that triage detected issues at scale — clustering and deduplicating failures, attributing root cause, routing to the right owners, and proposing fixes with supporting evidence for engineering review

• Ensure that your work is performed in accordance with the company's Quality Management System (QMS) requirements and contribute to continuous improvement efforts