# Lead Test Engineer - Scenario Coverage & Evaluation Data Sets

**Company:** [Avride](https://hotfix.jobs/companies/avride)
**Location:** Austin, TX
**Role:** QA Engineering
**Experience:** 8+ years
**Skills:** Software Testing, Test Engineering, SQL, Python, LLMs, Data Mining, Active Learning, Odd Taxonomy, Iso 34503, Iso 34501, Iso 34502, Sotif, Iso 21448, Lidar, Radar
**Posted:** 2026-08-19

> Leads end-to-end evaluation dataset and scenario coverage strategy for an autonomous-driving stack, using data mining, simulation, field capture, and risk-based metrics. Requires 8+ years in test engineering, full-system strategy ownership, hands-on SQL/Python, and strong cross-functional communication.

## Job Description

## 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.

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