Staff AI Scientist

GE Healthcare GE Healthcare · Healthcare · Bengaluru, Karnātaka, India · Digital Technology / IT

Staff AI Scientist role at GE Healthcare focused on developing and deploying AI for medical imaging. This role involves designing deep learning models, integrating foundation and multimodal AI, and ensuring solutions are scalable, compliant, and clinically relevant. Requires a PhD, strong deep learning framework experience, and familiarity with healthcare AI regulations.

What you'd actually do

  1. Design, develop, and deploy advanced deep learning models for medical image analysis, including detection, segmentation, classification, and quantification tasks across diverse imaging modalities.
  2. Leverage and adapt modern model architectures for data‑efficient model development.
  3. Explore and integrate foundation models and multimodal learning approaches that combine imaging with clinical text, reports, and metadata.
  4. Develop robust research prototypes with strong experimental rigor, evaluation metrics, and clinical relevance, under minimal supervision.
  5. Drive data-centric AI practices, including dataset curation, quality assessment, bias analysis, and annotation strategies at scale.

Skills

Required

  • PhD in Computer Science, Electrical Engineering, Biomedical Engineering, or a related field, or equivalent practical experience with a strong research track record
  • modern deep learning frameworks (e.g., PyTorch, TensorFlow)
  • model optimization techniques
  • medical image processing
  • experiment tracking
  • model versioning
  • reproducibility best practices

Nice to have

  • SimpleITK
  • MONAI
  • OpenCV
  • self-supervised learning
  • transfer learning
  • foundation models
  • multimodal AI applied to healthcare data
  • data governance
  • privacy-preserving AI

What the JD emphasized

  • publications in leading conferences, journals, or reputable public repositories in AI/ML or medical imaging
  • Demonstrated ability to translate research innovations into clinically meaningful, real‑world solutions
  • regulatory expectations
  • regulatory considerations in healthcare AI

Other signals

  • deploying advanced deep learning models for medical image analysis
  • translate cutting-edge research into scalable healthcare products
  • production-ready, scalable, and compliant AI systems