Job Title

Machine Learning Engineer (Medical Imaging AI) KEY POINTS High-impact role applying cutting-edge deep learning to real-world clinical problems (CT & MR imaging) ABOUT THE CLIENT Were supporting a globally recognised medical imaging technology company at the forefront of cardiovascular diagnostics, building intelligent software used by leading healthcare institutions worldwide. Their platform sits at the intersection of advanced imaging, clinical research, and machine learning combining large-scale medical datasets with state-of-the-art AI to improve how cardiovascular disease is diagnosed and understood. Following continued success and product expansion, they are investing heavily in their AI capability, with a particular focus on building robust, production-grade machine learning systems that directly impact patient outcomes. This is not a research lab environment detached from reality this is applied, high-stakes AI, where models are deployed into real clinical workflows and used by physicians globally. THE MACHINE LEARNING ENGINEER ROLE This is a highly technical, handson role focused on designing and building the core machine learning systems that power advanced cardiovascular imaging products. You will take ownership of key components across the ML lifecycle from data pipelines and model design through to deployment in production environments. The work is deeply challenging, sitting at the intersection of computer vision, 3D data, and biomedical science translating complex academic ideas into scalable, realworld systems. You will work closely with a small, highly capable team of engineers and scientists, contributing to both the research direction and the productionisation of models. Key responsibilities include: Designing and deploying deep learning models for 3D medical image segmentation and analysis Working with complex CT and MR imaging datasets to extract clinically meaningful insights Translating cuttingedge research into robust, productionready ML systems Building scalable pipelines from data ingestion through to model deployment Writing clean, welltested, highperformance Python code Collaborating with crossfunctional teams across engineering, product, and research Contributing to technical direction and model architecture decisions Staying close to the latest developments in medical imaging AI (e.g. MICCAI) and applying them where relevant ESSENTIAL SKILLS PhD in Computer Science, Machine Learning, AI, or a closely related field 03 years industry experience (or postdoctoral research) applying machine learning to realworld problems Strong grounding in deep learning, particularly computer vision or 3D data Experience working with complex datasets (ideally medical or scientific imaging) Proficiency in Python and at least one major ML framework (PyTorch or TensorFlow) Solid understanding of software engineering fundamentals (version control, testing, clean code) Evidence of translating research into practical implementations NICE TO HAVE Experience working with medical imaging data (e.g. DICOM) Exposure to 3D image segmentation or geometric modelling Familiarity with MLOps tooling (Docker, Kubernetes, MLflow) Experience deploying ML models in cloud environments (AWS, GCP, Azure) Publications in top-tier conferences (e.g. MICCAI, NeurIPS, CVPR) KEY SKILLS Machine Learning Engineer, Deep Learning, Medical Imaging, Computer Vision, 3D Image Segmentation, PyTorch, TensorFlow, Python, DICOM, MLOps, Healthcare AI, Research to Production #J-18808-Ljbffr