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
• 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
• Define and manage test strategies for new product features with a focus on testing rich E2E web experiences
• Use AI tools like Cursor to rapidly generate test cases from requirements, user stories, and code diffs. Leverage AI tools to speed up development, catch edge cases and improve accuracy of tests
• Integrate AI-driven static analysis, code coverage tools, and anomaly detection into CI/CD pipelines
• 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