Senior Machine Learning Engineer — Clinical Diagnostics

Hana Novak

Full-time · Senior · Copenhagen

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About the role

We are a Danish medical technology company. We have been developing diagnostic imaging and biomarker analysis systems for clinical use since 1998. Our products are CE-marked, FDA 510(k) cleared, and used in hospitals and diagnostic laboratories across 48 countries. We build for clinical environments, which means our quality management system is ISO 13485 certified, our software development process follows IEC 62304, and every ML model we deploy goes through a validation process that would satisfy a regulatory submission. We are not describing this as a burden. We are describing it as context. If you want to build ML systems that doctors and pathologists use to make decisions about real patients, this is what it looks like. We are expanding our AI development team and hiring a Senior ML Engineer to work on our histopathology image analysis platform — a system that assists pathologists in quantifying tumour markers, grading tissue, and detecting cellular anomalies in digitised slide images. The team currently has twelve engineers and four clinical scientists. The work is technically hard, the domain is genuinely interesting, and the mission is clear.

Responsibilities

Design and train deep learning models for tumour marker quantification and tissue grading on digitised histopathology slides
Own model validation from initial evaluation through regulatory submission support — you understand what a valid clinical evidence package requires
Collaborate with clinical scientists and pathologists to translate diagnostic criteria into model objectives and evaluation benchmarks
Contribute to the AI development framework: training infrastructure, model quality standards, and the documentation practices required by our QMS
Mentor mid-level engineers on clinical AI development practices and the reasoning behind regulatory requirements

Requirements

7+ years of ML engineering with at least 3 years in medical imaging, computational pathology, or a regulated clinical AI context
PyTorch for developing and training deep learning models on whole-slide image data — experience with patch-based training and multi-resolution architectures is directly relevant
Python at a senior engineering level: production-quality code, reproducible experiments, and the discipline to write analysis that a clinical scientist can verify
Docker for reproducible training and deployment environments that meet our ISO 13485 software quality requirements
MLflow for experiment tracking, model versioning, and maintaining the complete audit trail our regulatory submissions require
Statistics for rigorous clinical validation: sensitivity, specificity, AUC, inter-rater agreement, and the ability to design validation studies that would satisfy a notified body
Familiarity with IEC 62304 software lifecycle requirements or equivalent medical device software development standards is an advantage