Software Validation Engineer, Self-Driving

As a Software Validation Engineer on the Autopilot QA team, you will be responsible for validating the stability and performance of Autopilot software across Tesla's customer and robotaxi fleet. Your work will ensure that quality issues are detected early, triaged thoroughly, and resolved before they impact release timelines. You will define and review quality metrics, analyze automation outputs, and work directly with autopilot and tooling engineers to close gaps in test coverage and observability. This role sits at the intersection of QA engineering, data analysis, and developer tooling. You'll investigate failures across complex embedded systems, trace regressions through fleet telemetry and simulation data, and identify where automation can replace manual validation. A strong candidate is detail-oriented, technically rigorous, and thrives on the challenge of catching problems before they reach the fleet. You will collaborate with global engineering teams in the US, Europe, and Asia, which may require flexible hours to sync with distributed partners.

Responsibilities

Define, measure, and review quality and performance metrics to assess release readiness across customer and robotaxi builds Review automation outputs, investigate failures, and perform root-cause analysis with meticulous attention to detail Work directly with autopilot engineers to triage issues, scope fixes, and ensure timely resolution before release deadlines Identify gaps in data collection, test coverage, and observability tooling, and partner with tooling engineers to close them Build and improve validation scripts, analysis tools, and automation to strengthen the quality pipeline Drive cross-functional validation efforts, coordinating with software, firmware, and QA engineers to ensure comprehensive coverage Maintain and improve dashboards (e.g., Grafana) and alerting to surface quality signals clearly and accurately Track automation and quality improvement projects to completion, ensuring follow-through on open issues Analyze fleet telemetry, simulation results, and test data to detect patterns, edge cases, and areas of concern

Requirements

Evidence of solid coding skills in Python and 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 experience in QA engineering, test automation, or validation-focused development Strong analytical skills with meticulous attention to detail when reviewing data, metrics, and test results Strong understanding of SQL and telemetry analysis for performance and reliability evaluation Experience with dashboarding and observability tools (e.g., Grafana, Prometheus) Comfort working with large-scale, real-world telemetry data to assess software quality Experience with LLMs and AI-assisted engineering workflows, including prompt design and building automated skills or agents Excellent communication skills, with the ability to distill complex technical findings into clear action items for engineering teams Strong follow-through and organizational discipline, able to track multiple issues and drive them to resolution