AI Research Engineer

• 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.

• Experience in NLP with LLMs or VLMs

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.

• 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.