Staff AI Scientist

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

Staff AI Scientist role at GE Healthcare focusing on developing and deploying AI for medical imaging. The role involves designing deep learning models, leveraging foundation and multimodal AI, and collaborating with cross-functional teams to create scalable healthcare products. Requires expertise in deep learning frameworks, medical image processing, 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
  • Strong experience with modern deep learning frameworks (e.g., PyTorch, TensorFlow)
  • Hands-on expertise in medical image processing using libraries such as SimpleITK, MONAI, OpenCV, and related tools
  • Familiarity with self-supervised learning, transfer learning, foundation models, and multimodal AI applied to healthcare data
  • Experience with experiment tracking, model versioning, and reproducibility best practices

Nice to have

  • Publications in leading conferences, journals, or reputable public repositories in AI/ML or medical imaging
  • Working knowledge of data governance, privacy-preserving AI, and regulatory considerations in healthcare AI

What the JD emphasized

  • commercially deployed solutions
  • scalable healthcare products
  • strong research track record
  • publications in leading conferences, journals, or reputable public repositories in AI/ML or medical imaging
  • translate research innovations into clinically meaningful, real‑world solutions
  • regulatory expectations

Other signals

  • AI for medical imaging
  • commercially deployed solutions
  • foundation models
  • multimodal AI
  • scalable healthcare products