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About the Role

You will own test coverage for the autonomous driving stack, end to end. You will be responsible for defining what our evaluation dataset must contain for a release decision to be trustworthy, identifying gaps in coverage, and ensuring these gaps are addressed. This role involves a data-centric approach to testing, using various methods to generate and capture necessary driving scenes. You will lead through process development and deliver through existing QA engineers and operational resources, with the potential to grow your own team.

Responsibilities

Own the evaluation dataset, including its contents, missing elements, and implications for autonomous driving quality claims.
Maintain the coverage model, ensuring the scenario taxonomy and ODD parameter space accurately reflect the maturing technology and changing operating environment.
Bridge the gap between scenarios and scenes by selecting appropriate methods (mining, simulation, field data collection) for scene generation and managing associated costs.
Set the mining agenda for the QA team, guiding their search efforts, reviewing findings, and gathering feedback.
Act as the internal customer for mining tooling, providing requirements to development and analytics teams and maintaining the QA-engineering feedback loop.
Research and propose adoption of effective coverage, edge-case curation, and data selection techniques from other AV programs, research groups, and vendors.
Collaborate with dependent teams (labeling, analytics) by providing clear priorities and actionable feedback to ensure timely delivery of necessary data and metrics.
Report on coverage and readiness in a clear format suitable for release decisions, highlighting confidence levels and blind spots.

Requirements

8+ years in software testing or test engineering.
2+ years owning the test strategy for a full system.
Demonstrated understanding of coverage concepts, including equivalence classes, parameter spaces, and risk-based prioritization.
Proficiency in SQL and Python for data retrieval, joining, and sanity checking.
Fluent use of LLMs for tasks such as log search, triage, scenario generation, report drafting, and quick analysis.
Strong written communication skills, capable of producing reports for engineering leads and safety organizations.
Ability to reason precisely about US road rules and real-world driver behavior, or to quickly develop this capability.
Authorized to work in the United States.

Skills

SQL
Python
LLM usage
Data mining
Active learning
Data selection
Scenario-based testing frameworks
ODD taxonomy
SOTIF
Vehicle dynamics
Sensor modalities (LiDAR, radar, cameras)

Location

Austin, United States

Work Type

Onsite
Full-time

Experience Level

8+ years
2+ years owning test strategy

About the Company

Avride builds autonomous driving technology for vehicles and delivery robots.
Our QA organization measures how well that technology drives, largely in simulation.
We build our own datasets, track coverage, and expand coverage as technology advances.

Equal Opportunity

Avride is an equal opportunity employer and committed to providing reasonable accommodations to qualified applicants and employees with disabilities to ensure they have equal access to employment opportunities.
Avride complies with the Americans with Disabilities Act (ADA).
If you need a reasonable accommodation to assist with the application or hiring process, or to perform the essential functions of a job, please email jobs@avride.ai.