Full Description

What To Expect As a Tooling & Data Engineer on the Autopilot QA team, you will be responsible for designing and maintaining systems that analyze the stability and performance of Autopilot features across Tesla’s global fleet. Your work will enable rapid detection of regressions and high-confidence validation of vehicle software releases. You will build internal infrastructure, pipelines, dashboards, and automation to support cross-functional engineering teams in debugging and improving the world’s most advanced driver assistance system.

This role sits at the intersection of software engineering, QA, data engineering, and analytics. You’ll work with petabyte-scale telemetry, create observability tools, and automate regressions to ensure Autopilot is always getting safer and more reliable. A strong candidate has experience in backend or infrastructure engineering, a solid understanding of data systems, and a passion for enabling teams to move faster and smarter through tools.

You will collaborate with global engineering teams in the US, Europe, and Asia, which may require flexible hours to sync with distributed partners.

What You'll Do

Build and maintain scalable event analysis pipelines to process millions of in-vehicle signals daily Develop internal analytics tools to monitor Autopilot performance across the fleet and detect regressions automatically Design and maintain dashboards (e.g., Grafana) and alerting for stability, reliability, and performance metrics Collaborate with QA, software, and firmware engineers to detect, investigate, and track stability regressions Create tools that accelerate validation cycles via automated detection, triage, and reporting Work across infrastructure (e.g., Kubernetes) to ensure pipelines are reliable and easy to maintain Contribute to data science and analytics efforts using fleet data to detect patterns, anomalies, and edge cases Build test automation infrastructure for data-driven pre-merge validation, including PR blocking tools for regressions Propose and develop new internal tools to close feedback loops and improve quality across the Autopilot stack

What You'll Bring

Evidence of solid coding skills in Python or C++ Degree in Computer Science, Computer Engineering, Software Engineering, Information Systems, Electrical Engineering, Embedded Systems Engineering, or the equivalent in practical experience and evidence of exceptional ability Proven development experience, especially in tooling, automation, or backend development Strong understanding of SQL and telemetry analysis, including performance and reliability monitoring Experience with data pipeline frameworks and time-series dashboards (e.g., Grafana, Prometheus) Familiarity with Docker, Kubernetes, and other infrastructure tools for managing internal platforms Comfort working with large-scale, real-world telemetry and using it to drive product quality Passion for building tools that empower engineers and QA to work more effectively Strong attention to detail and a drive for automation, validation, and continuous improvement Excellent communication skills, with the ability to distill complex technical issues into clear action plans