AI quality assurance · Austin, Texas
I investigate how intelligent systems behave in the real world—especially at the edges, where ambiguity, safety, and user trust collide.
Expected: disclose uncertainty and request the missing constraint.
01 / POSITION
A response can be fluent and still be wrong. A system can pass the happy path and fail the person using it. I test beyond output formatting—probing context, consistency, recovery, escalation, and the consequences of a bad decision.
“What happens when the situation stops being obvious?”
02 / EVIDENCE
My experience connects system behavior, human impact, and useful reporting.
Execute structured on-road tests for new features, bug fixes, and release validation. Analyze driving data and logs to identify defects, edge cases, and safety risks, then communicate findings for engineering review.
Monitored autonomous vehicle performance during in-field scenarios, collected system data, and provided timely observations to engineers and operations leaders.
Investigated compliance and quality issues through root-cause analysis, interviews, calibration, and documentation review. Turned patterns into actionable coaching and process improvements.
Reproduced software, hardware, and network issues; isolated root causes; documented resolutions; and escalated defects with clear steps and follow-up.
03 / METHOD
Structure makes exploratory work explainable, reproducible, and useful.
Translate requirements into observable behavior and a clear test oracle.
Prioritize safety, trust, compliance, ambiguity, and high-impact failure paths.
Exercise normal, boundary, adversarial, underspecified, and contradictory cases.
Compare correctness, consistency, recovery, tone, and user consequences.
Report evidence, expected versus actual results, risk, and reliable reproduction steps.
04 / TEST LAB
Three illustrative scenarios show how I turn fuzzy AI behavior into testable questions.
These are portfolio examples, not claims about a specific employer or product.
05 / NEXT STEP
I bring hands-on QA, safety-critical observation, quality evaluation, technical triage, and clear communication to teams building systems people need to trust.