Company Description

At CluePoints, we’re redefining how clinical trials are run. As the premier provider of Risk-Based Quality Management (RBQM) and Data Quality Oversight software, we harness advanced statistics, artificial intelligence, and machine learning to ensure the quality, accuracy, and integrity of clinical trial data, helping life sciences organizations bring safer, more effective treatments to patients faster.

We’re proud to be an ambitious, fast-growing technology scale-up with a dynamic and diverse international team representing more than 20 nationalities. Collaboration, flexibility, and continuous learning are part of our DNA.

At CluePoints, you’ll find a culture where you can grow, make an impact, and have fun along the way. Guided by our values of Care, Passion, and Smart Disruption, we’re united by a shared mission: to create smarter ways to run efficient clinical trials and deliver AI-powered insights that improve human outcomes worldwide.

We are looking for a highly skilled Automation Tester with strong Playwright expertise whoembraces an AI-First approach to quality engineering. While automation will be your primaryfocus, we value professionals who maintain strong manual and exploratory testing capabilities and can adapt based on product needs.

You will play a critical role in designing scalable automation frameworks, leveraging AI tools likeGitHub Copilot/Codex to accelerate test development, and contributing to overall productquality through both automated and exploratory testing strategies. You will also help shape howwe adopt AI in quality engineering: building reusable AI skills and agents, setting standards forresponsible AI use, and mentoring colleagues as we move from simply using AI codingassistants to delivering governed, scalable AI-enabled testing capabilities

What You’ll Bring

• Strong hands-on experience with Playwright automation framework (JavaScript or TypeScript).
• Demonstrated use of AI coding assistants (GitHub Copilot, Codex, Claude, Cursor,ChatGPT, or similar) in real delivery, with governance and validation rather than pure consumption.
• Experience designing AI-native quality engineering solutions: prompt engineering, AI-assisted test generation, intelligent test maintenance, self-healing, and evaluation of AI outputs.
• Knoweldedge of building reusable AI skills / agents for testing use cases such as test generation, requirements analysis, failure analysis, exploratory testing, and releasequality assessment.
• Solid understanding of creating automation frameworks from scratch and design patterns.
• Experience integrating automated tests into CI/CD pipelines, including containerised test execution (Docker, K8s / K3s) and cloud-native test infrastructure.
• Experience with test management tools such as Zephyr, Xray, or similar.
• Strong understanding of software testing fundamentals (test design techniques, defectlifecycle, risk analysis, risk-based testing, shift-left, defect prevention).
• Proven experience in manual and exploratory testing of web applications.
• Ability to mentor and coach colleagues, set standards, and drive AI adoption acrosssquads.
• Strong stakeholder communication, able to influence engineering teams and leadership on automation strategy, AI adoption, and QE transformation.

(Nice to have)

• Regulation experience in the pharmaceuticals (clinical trials), Insurance / Banking / Fintech (advantageous)
  

What You’ll Be Doing

• Designing, developing, and maintaining scalable Playwright-based automation frameworks for UI, API, and end-to-end testing.
• Writing clean, maintainable, and reusable test code using TypeScript or JavaScript.
• Integrating automated test suites into CI/CD pipelines (GitHub Actions, Azure DevOps,Jenkins, etc.) with parallel, containerised execution (Docker, Kubernetes / K3s) and quality gates that hold the line without slowing delivery.
• Ensuring robust reporting, logging, traceability, and clear failure diagnostics.
• Continuously improving framework reliability, performance, and maintainability.
• Owning automation strategy across API, UI, integration, and end-to-end layers: risk-basedand aligned to the right level of the test pyramid.

• Leveraging AI coding assistants (e.g., GitHub Copilot, Codex, or similar tools) toaccelerate test development and framework enhancement.
• Using AI tools to generate high-quality test scenarios from requirements and user stories.
• Reviewing, validating, and refining AI-generated code to ensure accuracy, scalability, andmaintainability.
• Contributing to AI-driven test design patterns and reusable automation strategies.
• Promoting AI-augmented productivity practices within the QA team.
• Designing and building AI-native testing capabilities: reusable AI skills, agents, andworkflows for test generation, intelligent maintenance, self-healing, failure analysis, andrelease-quality assessment.
• Driving responsible AI adoption across squads by establishing standards for when AIgenerated code and tests can be trusted, reviewed, modified, or rejected.
• Optimising AI workflows for cost and quality: prompt engineering, context management,token/model selection, reuse of skills and agents, and safe autonomy limits.
• Mentoring engineers, running code reviews, and measuring the impact of AI-driven testingwith meaningful metrics (productivity, coverage, defect detection, maintenance effort, AIaccuracy, cost).

• Performing exploratory testing to uncover edge cases, usability gaps, and unexpected systembehavior.
• Designing and executing manual test scenarios when automation is not practical or during early-stage feature validation.
• Collaborating closely with product and engineering teams to clarify requirements and surface hidden assumptions.
• Identifying high-value automation candidates through exploratory testing sessions.