Senior QA Engineer, AI & Automation

Adventure is our Culture. Join a team that celebrates a lifestyle as bold as the terrain we love. At Backcountry, we are rooted in adventure, recognition, and wellbeing on and off the mountain. We spotlight employee stories, celebrate milestones, and offer exclusive outdoor perks. Whether you are at HQ, in a retail store, or remote, you will be part of a team that thrives on energy, exploration, and connection.

Reports to: Senior Retail Technology Architect

Location: Remote (US, Canada)

• 5+ years of experience in Quality Assurance and Testing, including 3+ years building and maintaining test automation that runs in CI/CD.
• Proven, hands-on experience with Playwright for web and API E2E automation; Cypress or Selenium experience is a plus.
• Strong automation fundamentals: ability to design, build, and maintain scalable, reliable test suites and frameworks, and to diagnose and eliminate flaky tests.
• Strong proficiency in TypeScript/JavaScript or Python (Java a plus); you write test code to the same standard as production code.
• Hands-on, day-to-day use of AI coding tools (Claude Code, GitHub Copilot, or Cursor) to write, refactor, and debug test code, with the discipline to validate and correct AI-generated output.
• Experience integrating automated tests into CI/CD pipelines (GitHub Actions preferred), including parallel execution, reporting, and quality gates.
• Solid experience with functional, regression, API, and performance/load testing (Postman, JMeter, k6, or equivalent).
• Strong exploratory testing skills and the judgment to build risk-based test strategies across automated, AI-assisted, and manual coverage.
• Demonstrated SDLC and Agile experience with defect lifecycle management using Jira and TestRail.
• Ability to operate effectively under a steady release cadence and manage competing priorities with minimal supervision.

• E-commerce QA automation experience, particularly in environments shipping frequently with CI/CD integration.
• Experience building or adopting agentic testing workflows (AI test generation, self-healing tests, MCP-connected tools, or LLM-based failure triage).
• Experience testing AI/ML-powered features such as search, recommendations, or LLM-generated content, including evaluation of non-deterministic output.
• Familiarity with BrowserStack or similar cloud device and browser grids.
• Experience with observability tooling like Grafana or GCP-based infrastructure, including using production signals to prioritize test coverage.