22 set - Roma
trtwo
Machine Learning Engineer - Medical ImagingSi assicuri di leggere attentamente le informazioni relative a questa occasione prima di inviare la sua candidatura.Career path toward Lead ML Engineer or Head of AI within 2-3 yearsIndustry SectorsHealthcare & Medical, Information Technology, Research & DevelopmentTechnologies you'll work withpowered by Job NautilusSalary60,000 - 80,000 EUR/year + equityJob DescriptionAn innovative company in the digital health sector is building the next generation of AI-assisted diagnostic tools and is looking for a Machine Learning Engineer specialising in medical imaging. You will develop and deploy deep learning models for radiology and pathology applications.You will work with a multidisciplinary team of doctors,
data scientists and engineers to bring AI from research to clinical practice.ResponsibilitiesDevelop and train deep learning models for medical image classification and segmentationBuild data pipelines for DICOM image preprocessing and augmentationCollaborate with radiologists for model validation and clinical testingDeploy models via containerised inference services with sub-second latencyStay current with latest research in medical AI and computer visionEnsure compliance with MDR and AI Act requirements for medical devicesMust-have RequirementsMSc or PhD in Computer Science, Biomedical Engineering or related field3+ years experience with deep learning frameworks (PyTorch preferred)Strong experience with CNNs, U-Net, Vision Transformers for image analysisExperience with medical imaging data (DICOM, NIfTI)Python proficiency and familiarity with MLOps toolsNice-to-have xysqume RequirementsPublications in medical imaging or computer vision conferencesExperience with FDA/CE marking processes for AI medical devicesKnowledge of federated learning approachesPersonal RequirementsPassion for applying AI to improve healthcare outcomesRigorous scientific approach with attention to reproducibilityAbility to communicate complex technical concepts to non-technical stakeholdersEthical mindset regarding AI in healthcare#J-18808-Ljbffr
23 set - Lecce
Snai
23 set - Tempio Pausania
Serafina
23 set - Grosseto
Il Globo Vigilanza
23 set - Bolzano
Kpmg Italy