Staff AI Scientist

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

Staff AI Scientist at GE Healthcare in Bengaluru, India, focusing on developing and deploying advanced deep learning and multimodal AI models for medical imaging analysis. The role involves translating research into scalable healthcare products, working with cross-functional teams, and adhering to regulatory expectations.

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
  • 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
  • Strong experience with modern deep learning frameworks (e.g., PyTorch, TensorFlow) and model optimization techniques
  • 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
  • Working knowledge of data governance, privacy‑preserving AI, and regulatory considerations in healthcare AI

Nice to have

  • medical image processing
  • self-supervised learning
  • transfer learning
  • foundation models
  • multimodal AI
  • experiment tracking
  • model versioning
  • reproducibility best practices
  • data governance
  • privacy-preserving AI
  • regulatory considerations in healthcare 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
  • working knowledge of data governance, privacy‑preserving AI, and regulatory considerations in healthcare AI

Other signals

  • deploying advanced deep learning models for medical image analysis
  • translate cutting-edge research into scalable healthcare products
  • drive data-centric AI practices
  • collaborate closely with software and MLOps teams to enable production-ready, scalable, and compliant AI systems