We're looking for a Quality Engineer who thinks beyond their test cases: someone who takes ownership of quality end-to-end, challenges what "done" looks like, and actively looks for better ways to validate and protect what ships. This role is ideal for an engineer with solid testing experience, but more importantly, someone with the right mindset: proactive, solution-oriented, and comfortable working across clinical, AI, and software teams to own coverage of complex features.
What you’ll do
In this role, you will define, build, and maintain the test strategy and automated pipelines that protect our medical software from requirement to release. You'll take end-to-end ownership of quality across the full feature lifecycle — from challenging specs early to signing off on coverage before anything reaches the clinic. Along the way, you'll drive automation efforts to reduce manual testing overhead and increase reliability, contribute to CI/CD pipelines and deployment validation, and continuously raise the bar on what "ready to ship" means.You'll also play a central role in setting quality standards across the team, while collaborating closely with clinical, AI, and software engineers to embed quality early — not as a final gate, but as a continuous practice throughout development.
Job requirements
- ~3-6 years of hands-on experience in software quality engineering.
- Experience in writing clear, concise test strategies, plans and test cases.
- Proven work experience with Python and scripting.
- Hands-on experience with automated testing tools such as Robot Framework.
- Excellent all round communication skills in English
- Familiarity working in a Linux environment
Preferred qualifications
- Experience with Docker containers.
- Experience with automated testing pipelines and CI/CD integration.
- Familiarity with AI-assisted development tools such as Claude Code or Cursor
What matters most to us is mindset. We're looking for someone who doesn't wait for bugs to appear — you anticipate quality risks early and drive them to resolution before they reach the clinic. You take ownership beyond your test cases, treating quality as a shared outcome rather than a checklist. You're curious and rigorous, willing to challenge specifications, question assumptions, and push back when something isn't ready to ship. You're comfortable working across clinical, AI, and software teams, and you genuinely care about continuously improving how we test, validate, and release.
This role goes beyond executing test plans. You'll be expected to influence how features are defined and built, identify gaps in coverage and process, and help shape both the test infrastructure and the quality culture in a meaningful way.
About us
ScreenPoint Medical is a leading company that develops and markets breast image analysis and cutting edge machine learning applications and services. Our product Transpara improves breast cancer survival rates by detecting cancers earlier so that treatment can be more effective and less invasive.
Do you want to help us build an innovative solution to improve health worldwide? And do you want to be part of an ambitious and fast-growing team who help you develop your career further? Please apply using the application button.
Providing a Certificate of Conduct (VOG) or background check is part of our application procedure. Questions about the contents of the vacancy or the recruitment process at ScreenPoint Medical? Please send an email to [email protected].
Skills Required
- Bachelor's degree in Computer Science, Engineering or equivalent
- Experience in writing clear, concise test plans and test cases
- Solid knowledge on Python and scripting
- Capable of working in a Linux environment
- Excellent all round communication skills in English
What We Do
ScreenPoint helps radiologists detect breast cancer confidently and faster. We develop and market image analysis and machine learning applications and services to improve early detection of breast cancer. Our flagship product - Transpara - has been evaluated in over 35+ peer-reviewed publications and has demonstrated consistent results, regardless of patient age, breast density, or ethnicity. Transpara uses the latest developments in machine learning such as Deep Learning and is trained with very large well curated databases of screening mammograms. Research and development at ScreenPoint is aimed at improving performance to the level of expert radiologists and beyond.
