Posted:6 days ago|
Platform:
On-site
Full Time
You will be instrumental in designing and developing cutting-edge AI/ML algorithms for image analysis, segmentation, classification, anomaly detection, and generative tasks in Magnetic Resonance Imaging (MRI).
· Lead end-to-end development of deep learning models for medical imaging tasks – _from data
curation and preprocessing to model training, evaluation, and deployment.
BioGPT, MedSAM) for use in diagnostic and clinical imaging applications.
· Drive research and prototyping of novel architectures for image segmentation, detection, and
generation (e.g., UNet variants, GANs, autoencoders, diffusion models).
· Collaborate cross-functionally with radiologists, product managers, software engineers, and
regulatory teams to ensure clinical relevance, robustness, and compliance.
· Contribute to the development of scalable ML pipelines, model interpretability tools, and
performance monitoring systems.
· Publish findings in peer-reviewed journals or conferences and represent the company at scientific
and industry forums.
· Mentor junior data scientists and guide the team on best practices in model development,
validation, and documentation.
PhD or master Degree in computer science or Biomedical Engineering, Applied Mathematics, or a related field.
· 5+ years of experience in data science or machine learning, with at least 3 years focused on
medical imaging.
· Strong experience in deep learning frameworks (TensorFlow, PyTorch) and model architectures for
computer vision.
adaptation.
· Proven ability to work with 2D/3D imaging datasets (DICOM, NIfTI), and medical imaging toolkits
(e.g., MONAI, SimpleITK, ITK-SNAP).
· Expertise in evaluation metrics specific to medical imaging (Dice, IoU, AUC, etc.) and experience
working with imbalanced datasets.
· Solid understanding of healthcare data compliance (HIPAA, FDA, MDR) and medical device AI/ML
lifecycle.
· Excellent problem-solving, communication, and leadership skills.
· Publications or patents in AI for healthcare or medical imaging domains.
· Experience with PACS/RIS systems, HL7/DICOM standards, and clinical workflows.
· Familiarity with LLMs or multimodal generative models in a clinical context.
· Exposure to MLOps, model deployment, and on-device inference optimization (e.g., TensorRT,
ONNX, OpenVINO).
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