Machine Learning Engineer

Medical Imaging AI Scale-Up | Germany | €95k – €115k + Equity | PermanentAI Futures has been engaged by the Co-Founder & CTO of one of Germany's best-funded medical imaging AI companies to build out the machine learning team. This is a senior individual contributor hire with a clear route to lead.The companyA venture-backed medical imaging company and CE-marked software live in radiology practices and hospitals across Europe. Their models read [CT and MRI/whole-slide pathology] in production, every day, on real patients.They are past the question of whether the technology works. The question now is whether it works everywhere: on every scanner, in every hospital, at a volume that doubles annually.The roleAs a ML Engineer you will own models end to end - from the training pipeline to what happens when a radiologist disagrees with the output on a Tuesday morning.This is production ML in a regulated environment. You build the model, you build the evaluation that proves it, and you own the evidence when a notified body asks how you know. The hard part is not the architecture. It is generalisation: a model that performs on your validation set and falls over on a scanner it has never seen is not a product.You will be working alongside in-house radiologists and pathologists who review the output and tell you, in detail, when it is wrong.What you'll doBuild and ship segmentation and classification models on 3D volumes or gigapixel whole-slide imagesOwn the evaluation framework - define what "good enough" means for a clinical claim, and prove it holds across sites, scanners and patient populationsWork directly with in-house clinicians on annotation strategy and edge-case reviewBuild the monitoring that catches performance drift after deployment, not beforeProduce the technical evidence that supports regulatory submission under EU MDRWhat you'll bringProduction ML, not research ML - you have shipped models people depend on, and you have been on the receiving end when one failedPython and PyTorch - essential. Experience with nnU-Net, MONAI or equivalent medical imaging frameworks a strong advantageMedical imaging data in the real world - DICOM that does not conform to spec, inconsistent tagging, ground truth two experts disagree onEvaluation rigour - you are as interested in how the model fails as in how it performsComfort working with clinicians who will challenge your output directlyDesirableEU MDR or FDA submission experienceWhole-slide image handling at gigapixel scale, or 3D volumetric segmentationFoundation models applied to medical imagingMLOps: MLflow, Kubernetes, cloud training infrastructurePackage & Details€95,000 – €115,000 base + equityPermanent | Hybrid.AI Futures have been engaged exclusively for this search.If this sounds like an exciting challenge to you please apply below.AI Futures | Filling the AI Skills Gap ®