Staff Machine Learning Engineer

GE Healthcare GE Healthcare · Healthcare · Haifa, Haifa District, Israel · Engineering / Technology

Staff Machine Learning Engineer at GE Healthcare focusing on designing, developing, and deploying production-grade AI systems for medical imaging products, specifically vision-language models, diffusion models, and multimodal learning. The role involves the full model lifecycle from data ingestion to deployment and monitoring, requiring strong ML/DL frameworks experience and software engineering fundamentals.

What you'd actually do

  1. Design, implement, and productionize AI models for clinical applications, including vision and diffusion models.
  2. Own end-to-end model lifecycle: data ingestion, training, evaluation, deployment, monitoring, and iteration.
  3. Work with large-scale medical datasets, ensuring data quality, preprocessing, and efficient pipeline management.
  4. Drive technical decisions and tradeoffs with a focus on scalability, maintainability, and real-world constraints.
  5. Collaborate with clinical AI scientists to translate research innovations into production-ready solutions.

Skills

Required

  • Master’s or PhD in Computer Science, Electrical Engineering, Biomedical Engineering, or related field.
  • Significant hands-on experience building and deploying ML systems to production.
  • Strong experience with machine learning and deep learning frameworks (e.g., PyTorch, TensorFlow).
  • Solid understanding of deep learning, computer vision, and natural language processing techniques.
  • Solid software engineering fundamentals, including testing, version control, CI/CD, and code reviews.

Nice to have

  • Prior experience working with medical imaging data is highly preferred.
  • Independent, self-learner, and results-oriented.
  • Fluent English (speaking and writing).

What the JD emphasized

  • production-grade AI systems
  • vision-language models
  • diffusion models
  • multimodal learning
  • end-to-end model lifecycle
  • large-scale medical datasets
  • medical imaging data

Other signals

  • production-grade AI systems
  • vision-language models
  • diffusion models
  • multimodal learning
  • end-to-end model lifecycle
  • large-scale medical datasets