Staff Ai/ml Engineer

GE Healthcare GE Healthcare · Healthcare · Seongnam, Gyeonggi-do, South Korea +1 · Digital Technology / IT

Staff AI/ML Engineer at GE Healthcare in Seongnam, Gyeonggi-do, Korea, Republic of. This role focuses on designing, developing, and delivering advanced data science and machine learning solutions to enhance product capabilities. The engineer will be responsible for hands-on model development, implementation, and integration into production systems, with a focus on scalable and reliable performance. The role involves end-to-end model lifecycle management, including data preparation, training, evaluation, validation, deployment, monitoring, and optimization. Collaboration with cross-functional teams and global teams is essential. Experience in healthcare data, medical imaging, or regulated environments is preferred.

What you'd actually do

  1. Lead, develop and implement machine learning models, statistical analyses, and AI algorithms to enhance product functionality and performance.
  2. Take ownership of end-to-end model development including data preparation, model training, evaluation, and validation.
  3. Implement scalable and efficient data pipelines to support model development and deployment.
  4. Collaborate with software engineers and system teams to integrate machine learning models into production systems.
  5. Support model deployment, monitoring, and performance optimization in production environments.

Skills

Required

  • Python
  • R
  • Pandas
  • NumPy
  • TensorFlow
  • PyTorch
  • Scikit-learn
  • data pipeline development
  • large-scale data processing
  • software development lifecycle
  • model validation
  • testing practices
  • object-oriented design
  • C++
  • C#
  • Azure
  • AWS
  • GCP
  • data platforms
  • English communication skills

Nice to have

  • Master’s degree in data science, Computer Science, AI, Statistics, or related field
  • healthcare data
  • medical imaging
  • regulated environments
  • C++
  • Python
  • image processing
  • medical image processing
  • CUDA
  • OpenCL
  • GPGPU programming
  • real-time software implementation
  • optimization techniques for running AI models on GPU
  • SQL
  • MLOps
  • model governance
  • explainable AI (XAI)
  • technical leadership
  • stakeholder engagement

What the JD emphasized

  • healthcare data
  • medical imaging
  • regulated environments

Other signals

  • model development
  • model implementation
  • production-ready solutions
  • integrate data-driven intelligence into products
  • scalable performance
  • model training
  • model evaluation
  • model validation
  • data pipelines
  • model deployment
  • model monitoring
  • performance optimization
  • MLOps
  • model governance
  • explainable AI (XAI)