Responsibilities

Coverage is a data problem before it is a testing problem. A scenario that exists on paper is not covered until there are real scenes behind it, and closing that distance is the craft of this role: metric-driven search across driving data, VLM-assisted retrieval and clustering, criticality-based selection, variation in simulation, and structured capture in the field. You will use that toolkit yourself before you ask anyone else to, and turn what works into something the whole QA team can run without you.
The mindset we're hiring for
Most testing work starts from a defined expected output, where the job is to confirm the system produced it. This role is different. The possible driving situations are effectively infinite, and for most of them there is no single right answer to check against.
We're looking for someone who thinks in terms of a space rather than a list: which parts of it we've sampled, which we haven't, what's rare but matters, and how you'd even notice that something is missing. If you've had to decide what to test when you couldn't test everything, and defend that decision, this role will feel familiar.
You don't need autonomous vehicle experience
The AV domain is learnable in a couple of quarters. The coverage instinct is what we can't teach. If you've done this kind of work under a different name, we want to hear from you:
Evaluation, validation, or simulation in AV, robotics, or perception
Detection engineering or threat detection, where the attack space is unbounded, there's no ground truth per event, and you're always trading false positives against false negatives
Search relevance or recommendations, building judged sets with coverage across query types
LLM or ML model evaluation, building eval sets and benchmarks and finding what they fail to catch
Speech recognition, medical AI, or fraud and risk modeling, curating test sets for the long tail, edge cases, and drift
Own the evaluation dataset. What is in it, what is missing, and what it lets us claim about autonomous driving quality.

Qualification

8+ years in software testingA track record of taking on moreCoverage as a first-class problemHands-on with dataStrong written communicationAutonomous vehiclesExperience with data mining

Required

8+ years in software testing, test engineering, validation, or evaluation, including 2+ years owning the test or evaluation strategy for a full system rather than a feature area.
A track record of taking on more. You've owned an area end to end, led or mentored engineers, and improved how your team works rather than only executing within it.
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.

Preferred

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). Helpful, not required.
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. Not required; you'll pick these up on the job.
Experience with data mining, active learning or data-selection loops over large sensor, log, or model-output datasets.
Experience specifying internal tooling and working with a platform team.
Experience with vehicle dynamics or sensor modalities (LiDAR, radar, cameras).
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.

Lead Test Engineer - Scenario Coverage & Evaluation Data Sets at Avride · LumiJobs

Lead Test Engineer - Scenario Coverage & Evaluation Data Sets

Responsibilities

Qualification

Required

Preferred

Company

Avride

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