Lead Test Engineer - Scenario Coverage & Evaluation Datasets

Own the evaluation dataset. What is in it, what is missing, and what it lets us claim about autonomous driving quality.

Own the coverage model. Our scenario taxonomy and ODD parameter space have to stay accurate as the technology matures and the operating environment changes. You own that: spotting where the model no longer describes what our vehicles actually meet, and working with the QA engineer who owns each category to keep it current.

Close the distance between scenarios and scenes. For each thin area, choose the method that will actually produce the scenes you need — mining, simulation or the field — and know what it costs before you spend it.

Set the mining agenda for the QA team. Decide what each area owner should be looking for next, review what comes back, and keep a regular rhythm for collecting their feedback.

Be the internal customer for our mining tooling. Use it yourself, turn what you learn into requirements for the development and analytics teams, and keep the feedback loop between QA and engineering running so the tooling keeps pace with what we ask of it.

Bring in what works elsewhere. Track how other AV programs, research groups and vendors solve coverage, edge-case curation and data selection, and turn what is worth having into concrete proposals.

Work through the teams you depend on. Coverage is only visible once scenes are labeled and only measurable when the right metrics exist, which makes labeling and analytics standing partners rather than occasional ones. Give them clear priorities and specific, actionable feedback, and stay close enough to see early when something you depend on is at risk.

Report coverage and readiness. Put them in a form a release decision can be made on — clear about confidence and about blind spots.

8+ years in software testing or test engineering, including 2+ years owning the test strategy for a full system rather than a feature area.

Coverage as a first-class problem. You can talk about equivalence classes, parameter spaces, risk-based prioritization, and what would have to be true for a dataset to be enough.

Hands-on with data. SQL and Python at the level where you pull, join and sanity-check a metrics dataset yourself rather than filing a ticket.

Fluent use of LLMs as a working tool — log search, triage, scenario generation, report drafting, quick analysis scripts.

Strong written communication. Your reports are read by engineering leads and by our safety organization.

Ability to reason precisely about US road rules and real-world driver behavior — or to get there quickly.

Experience hiring QA engineers — screening, interview loops, and hire/no-hire calls you stand behind, whether into your own team or a partner team.

Autonomous vehicles, robotics, ADAS — or another safety-critical domain (aerospace, medical devices, rail, industrial automation).

Familiarity with scenario-based testing frameworks: ODD taxonomy (ISO 34503), scenario-based safety evaluation (ISO 34501/34502), and the SOTIF (ISO 21448) known/unknown × safe/unsafe framing.

Experience with data mining, active learning or data-selection loops over large sensor/log datasets.

Experience specifying internal tooling and working with a platform team.

Experience with vehicle dynamics or sensor modalities (LiDAR, radar, cameras).

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Candidates are required to be authorized to work in the U.S. The employer is not offering relocation, sponsorship, and remote work options are not available.

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.