Staff Machine Learning Engineer, GE HealthCare

Staff Machine Learning Engineer, GE HealthCare

Company GE HealthCare
Job title Staff Machine Learning Engineer
Job location Bellevue, Washington, United States of America
Type Full Time

Responsibilities:

  • Lead the design and implementation of advanced machine learning models, with a particular emphasis on Large Language Models (LLMs) and Computer Vision Machine Learning (CVML), to automate and enhance clinical tasks utilizing diverse data sets like medical images, electronic health records, patient waveforms, and clinical reports.
  • Ensure the precision and applicability of algorithms across varied patient groups through comprehensive statistical analysis and error estimation.
  • Spearheaded the development and refinement of prototypes, overseeing their evolution into scalable, efficient, and production-ready software.
  • Maintain and expand your expertise in the latest AI technologies and algorithms, driving innovation within the team.
  • Facilitate the development of versatile software and data infrastructures, emphasizing reusability, scalability, and integration across various teams and projects.

Requirements & Skills:

  • Master’s Degree in a “STEM” major (Science, Technology, Engineering, Mathematics) or equivalent field plus 5 years AI development for industrial applications in a commercial setting OR Ph.D. in a “STEM” major (Science, Technology, Engineering, Mathematics) or equivalent field plus 3 years AI development for industrial applications in a commercial setting.
  • Deep expertise in a specialized area of computer science such as Natural Language Understanding, Computer Vision, Machine Learning, Deep Learning, or Algorithmic Optimization, coupled with strong software development skills.
  • Mastery of one or more general-purpose programming languages (e.g., Python, Java, C/C++).
  • Demonstrated experience in leading the deployment of applications to production environments, adhering to the highest standards of operational excellence.
  • Profound experience with complex, noisy medical datasets and the unique challenges they present.
  • Expertise in managing large-scale data processing, advanced AI model training techniques, prompt tuning, model distillation, enhancing robustness, and model quantization.

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