AI-First Senior QA Engineer, Everly Health

• 4–6+ years of experience in QA engineering, software testing, or quality automation
• Strong experience with automated testing frameworks (e.g., Cypress, Playwright, Selenium)
• Demonstrated usage of AI tools to accelerate or enhance QA processes — such as Cursor, Copilot, Testim, CodiumAI, Diffblue, or custom LLM-based tools
• Familiarity with writing, maintaining, and optimizing automated tests for modern web applications (React/Next.js preferred)
• Deep understanding of CI/CD, test environments, and software deployment practices
• Strong analytical and debugging skills — able to quickly identify root causes and work cross-functionally to resolve issues
• Proactive communicator who thrives in a collaborative, high-velocity environment

Desirable

• We’re looking for a Senior QA Engineer who thrives in an AI-first engineering culture — one where artificial intelligence is not just a tool, but a force multiplier that enhances speed, creativity, and coverage in quality assurance
• In this role, you'll own the development and automation of test cases and test infrastructure, and you’ll leverage AI to accelerate and augment every aspect of QA: from generating tests based on product specs to writing and maintaining automated test suites, and surfacing insights from bugs and data anomalies
• You’ll collaborate closely with engineering, product, and design to ensure a culture of quality is embedded from day one, and your AI-enhanced workflows will set the bar for what a modern QA practice can achieve
• Use AI tools like Cursor to rapidly generate test cases from requirements, user stories, and code diffs
• Build and maintain automated end-to-end test suites using frameworks like Cypress, Playwright, or Selenium, with AI assistance to speed up development and catch edge cases
• Integrate AI-driven static analysis, code coverage tools, and anomaly detection into CI/CD pipelines
• Define and manage test strategies for new product features with a focus on AI-augmented speed and accuracy
• Use LLMs and agents to simulate user behavior for broader scenario coverage
• Continuously evaluate and implement AI-powered QA platforms to improve regression, smoke, and exploratory testing
• Collaborate with engineers on shift-left testing practices, pairing during development to catch issues early
• Monitor application performance, errors, and logs using tools like Sentry, DataDog, or equivalent — augmented by AI-powered alerting and triage
• Contribute to defining AI-first QA best practices, playbooks, and tooling standards across the team