AI Research Engineer

In the fight against breast cancer, every medical image is an opportunity: to unlock insight, to uncover risk, to embody health, to empower life. ScreenPoint’s Transpara Breast AI delivers unmatched precision for breast radiologists and helps define personalized care pathways for every woman we serve. Make your mark as part of The Breast AI Company.

We are looking for an enthusiastic and motivated AI Research Engineer to strengthen our Innovation team in building the foundation of ScreenPoint’s future AI models. For this role we look for someone who can develop foundation models and has experience with state-of-the-art methods (vision transformer networks, vision-language models). These methods will be used in downstream applications to solve a broad range of clinically relevant problems in breast cancer care in close collaboration with clinical experts.

You’ll be part of a highly motivated and ambitious group of experts in breast imaging, artificial intelligence and clinical research, who work closely together in an informal atmosphere to make our mission become a reality. You will work with a broad range of technologies and have the opportunity to learn and grow on a daily basis.

Your responsibilities

Design and implement downstream AI models that operationalize clinical endpoints using outputs from foundation models.

Translate clinical study designs and outcome definitions into clear modeling tasks and evaluation frameworks in collaboration with Clinical Scientists.

Define and apply consistent modeling and evaluation approaches across multiple biomarkers and imaging modalities.

Collaborate closely with the AI Algorithm Lead to ensure robust integration between foundation models, MLOps infrastructure, and downstream biomarker models.

Guide and review modeling approaches developed by AI Research Scientists, providing technical feedback and mentorship.

Perform in-depth model analysis, including calibration, subgroup performance, and failure-mode assessment.

Actively use AI-assisted tools and workflows to accelerate research, coding, experimentation, documentation, and analysis, while continuously identifying opportunities to improve team processes through AI.

Experience and skills

MSc or PhD in Computer Science, Machine Learning, Biomedical Engineering, Applied Mathematics, Physics, or a related technical field

At least 5 years of experience developing AI or machine learning models in a medical imaging or clinical research context

Proven ability to translate clinical or scientific questions into appropriate modeling approaches (e.g. classification, risk prediction, longitudinal modeling)

Experience working with multimodal data (e.g. imaging combined with clinical or pathology data)

Strong understanding of model evaluation, calibration, robustness, and subgroup performance in real-world datasets

Familiarity with foundation models and downstream fine-tuning or adaptation strategies

Proficiency in Python and deep learning frameworks, and experience working in Linux-based environments

An AI-native mindset, with demonstrated ability to effectively use tools such as Claude, ChatGPT, coding copilots, or similar technologies to improve the quality, speed, and efficiency of day-to-day work.

Preferred qualifications

Experience in NLP with LLMs or VLMs

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] . Please note that applications submitted via this email address will not be reviewed. To apply, please use the ‘Apply for this job’ button.