AI QA Engineer

Employment type

Full-time

Experience

3+ years

Location

Remote

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About the role

We are looking for an AI QA Engineer to help us build AlwaysQA as a post-deploy QA layer for teams working with coding agents.

You will design and develop checks for critical application flows such as signup, login, checkout, onboarding, forms, permissions, dashboards, and regressions after bug fixes. You will work on failure diagnosis, deployment history, check versioning, and a fix-and-rerun workflow that returns test results to a developer or coding agent.

This role is for someone who wants to build practical QA for real applications—not a generic browser-agent platform or a browser automation demo.

Responsibilities

  • Design critical checks for user flows in web applications.
  • Build failure-diagnosis logic using screenshots, logs, console output, network traces, and reproduction steps.
  • Develop deployment-history, check-versioning, and fix-and-rerun workflows.
  • Work with backend and infrastructure teams on a provider-independent runner abstraction.
  • Design QA-specific MCP actions for coding agents.
  • Generate fix-ready reports that help developers and agents resolve regressions faster.
  • Analyze flaky failures and issues involving test data, authentication, CAPTCHA, 2FA, staging, and production environments.
  • Document check behavior, limitations, design decisions, and risk scenarios.
  • Translate customer needs into practical, maintainable checks.

Requirements

  • Practical experience with QA automation, Playwright, browser testing, or test engineering.
  • Ability to analyze web application failures using logs, traces, network activity, the DOM, and observed user behavior.
  • Working knowledge of backend systems, REST APIs, authentication, test data, and integration patterns.
  • Experience with TypeScript or Python and automation.
  • Familiarity with Docker, CI/CD, and local development environments.
  • Understanding of data privacy, access control, and safe handling of test environments.
  • Ability to analyze complex problems and design practical, maintainable solutions.
  • Clear communication with both technical and non-technical colleagues.
  • Independence and ownership of technical work.

Helpful experience

  • Playwright traces, GitHub Actions, deployment webhooks, or application monitoring.
  • MCP, coding agents, developer tools, or issue-tracker integrations.
  • Authentication, authorization, SSO, and common challenges in testing authenticated user flows.
  • Browserbase, Playwright browsers, cloud runners, or similar execution infrastructure.
  • B2B security, compliance, and audit requirements.

What you will work on

  • Checks for critical web application flows.
  • Deployment history and result comparison across deployments.
  • Failure diagnosis using Evidence such as screenshots, logs, traces, and reproduction steps.
  • Check versioning and scenario-change detection.
  • Fix-and-rerun workflows for developers and coding agents.
  • QA-specific MCP actions for importing, running, diagnosing, and rerunning checks.
  • A provider-independent runner abstraction that supports multiple execution backends.

What we offer

  • Work on a real QA product used after deployments—not a generic automation demo.
  • The opportunity to build critical checks, failure diagnosis, and coding-agent workflows from the ground up.
  • Direct influence over product architecture, technical standards, and how QA is delivered.
  • Work spanning QA automation, backend systems, infrastructure, agent workflows, and security.
  • A flexible working model.
  • A development budget for QA automation, software architecture, security, and infrastructure courses.
  • Access to modern developer tools, runners, and experimental environments.
  • Knowledge-sharing sessions with engineers, architects, and QA experts.
  • Close collaboration with customers to protect their most important flows.
  • A growth path toward Senior AI QA Engineer, QA Platform Engineer, or Product Engineer.

About AlwaysQA

AlwaysQA helps teams that ship with coding agents verify critical flows after deployment.

We are not building a generic browser-agent platform. Browser infrastructure runs the browser. AlwaysQA knows what the application should do, checks it after deployment, and returns failures to the developer or coding agent.

We are building for teams that want to find, diagnose, and fix regressions in their most important user journeys sooner.

How to apply

Send us your CV, LinkedIn profile, GitHub profile, or examples of projects involving QA automation, Playwright, test infrastructure, or developer tools.

We are especially interested in projects involving end-to-end tests, browser automation, CI/CD, failure diagnosis, application monitoring, MCP, or coding-agent integrations.