Responsibilities Design, build, and maintain automated test suites (UI, API, integration) using AI-assisted tools to accelerate test-case generation, maintenance, and self-healing of flaky tests Own end-to-end QA for assigned feature areas — test planning, test-case design, exploratory testing, and regression coverage, not just automation Evaluate and integrate AI/ML-based testing tools (e.g., AI test-generation copilots, visual-regression AI, anomaly detection in test results) into the existing QA pipeline Triage, log, and track defects through resolution; collaborate directly with developers on root cause and fix verification Build and maintain CI/CD test integration so automated suites run reliably on every build Analyze test results and production signals to identify quality risk areas before they become customer-facing defects Mentor other QA engineers on applying AI tooling effectively without sacrificing test rigor Required Qualifications 6-9 years of QA engineering experience, including at least 2+ years building and maintaining test automation frameworks (Selenium, Playwright, Cypress, or equivalent) Hands-on experience using AI-assisted testing or development tools (e.g., GitHub Copilot, Cursor, AI test-generation platforms) in a production QA workflow Strong scripting ability in Python, JavaScript/TypeScript, or Java for building and maintaining automated tests Experience with API testing (Postman, REST Assured, or similar) and CI/CD pipelines (Jenkins, GitHub Actions, GitLab CI) Comfortable owning full QA cycles beyond automation — test planning, defect management, exploratory testing, release sign-off Familiarity with AI-assisted code review and automated testing/quality tools Comfort using AI-enabled project management tools (e.g., Jira AI, Asana Intelligence) for sprint tracking and status reporting Able to work onsite in Newark, CA Preferred QA experience in automotive, embedded systems, or another hardware-adjacent software environment Experience with performance and load testing tools