QA Automation Engineer/Test Automation Engineer

Margins is an award-winning AI and software engineering company helping organizations turn AI opportunities into production-ready solutions and measurable business outcomes.

We work across the full AI delivery lifecycle - from strategy and discovery through custom AI/software development, deployment and ongoing optimization.

Our teams build complex web and mobile platforms, enterprise software, AI agents, LLM applications, RAG systems, workflow automation and other production-grade AI solutions for clients across the US and Europe.

We are looking for an experienced Test Automation Engineer who believes AI is fundamentally changing how software quality should be done.

We are looking for someone who combines strong software testing fundamentals with automation engineering and an AI-first approach to QA.

You will work across Margins and client projects to establish testing strategies, build automated testing infrastructure and help engineering teams ship high-quality software faster.

We expect you to actively use modern AI development tools to multiply your effectiveness — generating and maintaining automated tests, identifying edge cases, analyzing failures, creating test data, understanding unfamiliar codebases and eliminating repetitive QA work.

The goal isn’t to automate everything.

The goal is to determine what should be tested, what should be automated and how AI can make the entire quality process dramatically more efficient.

• Establish QA processes and testing strategies on new client projects
• Understand complex product workflows and identify the highest-risk areas
• Define test plans, regression strategies and release criteria
• Perform exploratory and risk-based testing where human judgment provides the most value
• Improve release confidence without unnecessarily slowing engineering velocity
• Communicate quality risks clearly to engineering teams, product managers and clients

• Design, build and maintain automated E2E and regression test suites
• Automate testing and validation
• Create reusable testing infrastructure across projects
• Integrate automated tests into CI/CD pipelines
• Implement appropriate release gates and automated quality checks
• Identify flaky tests and continuously improve automation reliability

We specifically want someone who actively experiments with how AI can improve software testing.

You should be comfortable using tools such as Claude Code, Cursor, Codex, GitHub Copilot or similar AI development tools to:

• Generate automated tests from requirements, user stories and existing code
• Generate boundary conditions and unexpected edge cases
• Create realistic test data and test fixtures
• Analyze failed tests, logs and stack traces
• Accelerate root-cause investigation
• Maintain and refactor existing automation suites
• Convert discovered bugs into permanent regression tests
• Reduce repetitive manual QA work
• Explore autonomous and agent-based QA workflows

We don’t expect a specific AI tool. We care about whether you have developed an AI-native way of working and can demonstrate that AI meaningfully increases your output.

Depending on the project, you may work across:

• Complex web applications
• Mobile applications
• REST and GraphQL APIs
• Microservices
• SaaS platforms
• Enterprise integrations
• Real-time workflows
• Cloud infrastructure
• AI-powered applications

You should be comfortable validating not only what appears on screen but also what happens underneath it. 

All Margins’ work involves AI systems. You will help us develop even better approaches for testing:

• LLM applications
• RAG and enterprise knowledge systems
• AI agents and multi-step agentic workflows
• Prompt and model regressions
• Structured LLM outputs
• Hallucination and groundedness
• AI workflow reliability
• Tool/function calling
• AI-generated outputs against expected criteria
• Evaluation datasets and automated AI evals

Previous AI/LLM testing experience is a strong advantage, but deep curiosity and the ability to rapidly learn this emerging discipline are equally important.

• 2+ years of experience in software QA, test engineering or test automation
• Strong understanding of software testing fundamentals
• Strong experience with automated testing of modern web applications
• Hands-on experience with Cypress, Selenium or similar frameworks
• Ability to write clean automation code in TypeScript/JavaScript, Python or another modern language
• Experience testing REST APIs
• Experience with Postman or similar API testing tools
• Strong regression and exploratory testing skills
• Experience working with Git
• Understanding of modern software development and release processes
• Strong debugging and root-cause investigation skills
• Ability to independently understand complex product workflows
• Strong written and verbal English
• Ability to work autonomously and take ownership of quality

• Deep test automation experience
• CI/CD testing experience with GitHub Actions, GitLab CI, Jenkins or similar
• SQL/database testing experience
• AWS or Azure experience
• Docker experience
• Experience with Sentry, CloudWatch or similar monitoring/logging platforms
• Performance/load testing experience
• Experience testing enterprise integrations
• Experience working directly with clients
• Startup or fast-moving product development experience
• Claude Code, Cursor, Codex, GitHub Copilot
• AI-assisted test generation
• AI-assisted debugging
• Agentic development workflows
• Automated browser agents
• LLM evaluation frameworks
• RAG/LLM testing
• Prompt regression testing

We value people who:

• Think like engineers, not test-script executors
• Take ownership instead of waiting for detailed instructions
• Understand that quality is a team responsibility
• Challenge inefficient processes
• Experiment with new tools
• Automate repetitive work
• Communicate problems together with potential solutions
• Are comfortable working across different technologies, products and industries