Lead Test Engineer - Scenario Coverage & Evaluation Data Sets

Responsibilities

• Own the autonomous-driving evaluation dataset, including its contents, gaps, and the quality claims it supports.
• Maintain the coverage model, scenario taxonomy, and ODD parameter space as the technology and operating environment evolve.
• Close gaps between abstract scenarios and real scenes through data mining, simulation, or field capture.
• Set data-mining priorities for QA engineers, review results, and establish a recurring feedback process.
• Act as the internal customer for mining tools; use them directly and translate findings into requirements for development and analytics teams.
• Research coverage, edge-case curation, and data-selection practices from autonomous-vehicle programs, research groups, and vendors.
• Coordinate with labeling and analytics teams to establish priorities, metrics, and actionable feedback.
• Report coverage, readiness, confidence, and blind spots to support release decisions.
• Lead through process and existing QA resources, with the opportunity to build a team as scope expands.

Requirements

• 8+ years in software testing or test engineering, including 2+ years owning full-system test strategy.
• Experience with equivalence classes, parameter spaces, risk-based prioritization, and dataset sufficiency.
• Hands-on SQL and Python for extracting, joining, and validating metrics datasets.
• Practical use of LLMs for log search, triage, scenario generation, report drafting, and analysis scripting.
• Strong written communication for engineering and safety audiences.
• Ability to reason precisely about US road rules and real-world driver behavior, or learn them quickly.
• Authorized to work in the United States.
• Onsite work at the Austin headquarters; remote work is not available.

Nice to Have

• Experience hiring QA engineers.
• Experience in autonomous vehicles, robotics, ADAS, or another safety-critical domain.
• Familiarity with ODD taxonomy (ISO 34503), scenario-based safety evaluation (ISO 34501/ISO 34502), and SOTIF (ISO 21448).
• Experience with data mining, active learning, or data-selection loops over large sensor or log datasets.
• Experience specifying internal tooling with platform teams.
• Familiarity with vehicle dynamics or LiDAR, radar, and camera sensor modalities.

Compensation and Benefits

• The employer does not offer relocation or sponsorship.