Hi, we’re Gravie. Our mission is to create health benefits that actually benefit small and midsize businesses and their employees. Our innovative benefit solutions and services are developed and delivered by a diverse group of unique people. We encourage you to be your authentic self - we like you that way.

We improve the way employers offer health benefits and provide employees coverage they can use. The Staff AI Test Automation Engineer at Gravie will take hands-on ownership of our AI-driven test automation systems across our product portals. The Playwright framework and migration from legacy tooling is already underway, we need someone who can pick up this work, drive it to completion, and continuously improve, scale, and optimize it as we go. Rather than manually writing test cases, you will build on the frameworks, feedback loops, and infrastructure that enable AI tools to generate, execute, and maintain automated tests at scale. You will work closely with our QA, Engineering, and Product teams to dramatically increase our automated regression coverage, accelerate delivery timelines, and reduce the risk of defects reaching production. Agentic AI is central to this role — you'll be hands-on with AI agents that generate, execute, and iterate on tests with minimal manual intervention.

Core responsibilities:

• Continue building and hands-on improve AI-driven test automation workflows using tools like Claude Code, where AI agents generate, execute, and iterate on Playwright tests with minimal human intervention. Your primary job is building and refining the system, not writing individual tests.

• Continue evolving the existing Playwright test framework, refining patterns, page-object models, fixtures, and conventions to make them more efficient, scalable, and better optimized for AI-assisted test generation.

• Build and improve tight feedback loops between local test execution, CI pipeline results, and merge request creation, so AI agents can run tests, interpret failures, fix issues, and resubmit with minimal manual intervention.

• Drive the ongoing migration from Geb/Spock to Playwright to completion, using AI agents to accelerate conversion at scale while maintaining coverage continuity.

• Integrate and continuously optimize Playwright test execution in GitLab CI/CD pipelines with automated quality gates on every merge request and deployment. Optimize for fast feedback through parallelization and caching strategies.

• Instrument tests with reporting on pass/fail trends, flakiness rates, and coverage gaps. Use this data to prioritize AI-driven test generation toward the highest-value uncovered areas.