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're hiring a Senior Test Engineer to help shape the next generation of AI-first quality engineeringat CluePoints. This is not a scripts-and-tickets role — you'll architect Playwright automation frameworks, build reusable AI skills and agents that accelerate the whole QE function, and set thestandards for how our engineers responsibly adopt AI in their day-to-day. You'll partner with squads across the engineering org, mentor testers and developers, and move theteam from simply using AI coding assistants to building governed, scalable AI-enabled qualityengineering capabilities.

What You’ll Bring

• Extensive hands-on experience architecting scalable Playwright automation frameworks for enterprise-grade web applications — maintainability, reusability, low-flake design.
• Expert-level TypeScript / JavaScript with modern software engineering principles, design patterns, and clean-code discipline.
• Demonstrated use of AI coding assistants (GitHub Copilot, Codex, Claude, Cursor, ChatGPT, or similar) in real delivery — with governance and validation, not just consumption.
• Proven experience designing AI-native quality engineering solutions: prompt engineering, AI-assisted test generation, intelligent test maintenance, self-healing, and evaluation of AI outputs.
• Hands-on experience building reusable AI skills / agents for testing use cases — test generation, requirements analysis, failure analysis, exploratory testing, release-quality assessment.
• Strong integration of automation into CI/CD, containerised test execution (Docker, K8s / K3s), and cloud-native test infrastructure.
• Deep grounding in software quality engineering — risk-based testing, test design techniques, exploratory testing, shift-left, and defect prevention.
• Ability to mentor and technically lead — architecture reviews, coaching, standards, and driving AI adoption across squads.
• Strong stakeholder communication — able to influence engineering teams and leadership on automation strategy, AI adoption, and QE transformation.

What You’ll Be Doing

• Architect and evolve scalable Playwright automation frameworks in TypeScript / JavaScript — modular design, POM / Screenplay, API abstraction layers, reusable utilities, and low-flake stability.
• Design and build AI-native testing capabilities: reusable AI skills, agents, and workflows for test generation, intelligent maintenance, self-healing, failure analysis, and release-quality assessment.
• Drive responsible AI adoption across squads — establish standards for when AI-generated code and tests can be trusted, reviewed, modified, or rejected.
• Integrate automation into modern CI/CD pipelines with parallel, containerised execution (Docker, Kubernetes / K3s) and quality gates that hold the line without slowing delivery.
• Own automation strategy across API, UI, integration, and end-to-end layers — risk-based, aligned to the right level of the test pyramid.
• Optimise AI workflows for cost and quality: prompt engineering, context management, token/model selection, reuse of skills and agents, and safe autonomy limits.
• Mentor engineers, run architecture and code reviews, and measure the impact of AI-driven testing with meaningful metrics — productivity, coverage, defect detection, maintenance effort, AI accuracy, cost.