Senior AI/ML Engineer

Requirements

• Bachelor's degree in data science, Computer Science, Statistics, Engineering, or related field.
• 8~12 years of experience in data science, machine learning, or advanced analytics.
• Strong experience in machine learning model development, evaluation, and deployment.
• Proficiency in programming languages such as Python, R, or similar, along with data handling frameworks (e.g., Pandas, NumPy)
• Hands-on experience with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn)
• Experience with data pipeline development and large-scale data processing technologies.
• Understanding of software development lifecycle and model validation/testing practices.
• Experience in object-oriented design methodology and various programming languages, including C/C++, C#
• Working knowledge of cloud environments (Azure, AWS, or GCP) and data platforms
• Business-level English communication skills for global collaboration.
• Master's degree in data science, Computer Science, AI, Statistics, or related field.
• Experience in healthcare data, medical imaging, or regulated environments.
• Demonstrated experience in C++/Python especially in image processing / medical image processing domain.
• Familiarity with CUDA/OpenCL/GPGPU programming.
• Hands-on Experience in implementing real-time software.
• Knowledge of optimization techniques for running AI models on GPU
• Strong working knowledge of SQL for data querying.
• Strong knowledge of MLOps, model governance, and explainable AI (XAI).
• Inclusion and Diversity
• We expect all employees to live and breathe our behaviors: to act with humility and build trust; lead with transparency; deliver with focus, and drive ownership - always with unyielding integrity.
• #LI- JJ1
• #LI-ONSITE
• Additional Information
• Relocation Assistance Provided: No

Benefits

Additional Information

Job Description Summary The Senior AI/ML Engineer is responsible for designing, developing, and delivering advanced data science and machine learning solutions to enhance product capabilities. This role focuses on hands-on model development and implementation while working closely with architects to translate high-level AI strategies into production-ready solutions. The candidate will collaborate with cross-functional teams to integrate data-driven intelligence into products and ensure reliable and scalable performance in real-world environments. Job Description [HR Talk] GE헬스케어 성남 초음파 연구소에서 AI 및 머신러닝 기술을 활용해 제품의 혁신을 이끌어갈 전문가를 찾고 있습니다. 데이터 기반 솔루션과 머신러닝 모델을 직접 개발하고 다양한 팀과 협업하여 AI 기능을 제품에 적용하는 역할입니다. 실제 서비스 환경에서 안정적이고 확장 가능한 AI 솔루션을 구현하며 기술적 리더십을 발휘할 수 있는 포지션입니다. 초음파 장비 혹은 의료기기에서 유사한 경험이 있는 후보자를 우대하며 영어커뮤니케이션이 원활한 후보자를 선호합니다. Roles and Responsibilities Duties include (but are not limited to): Develop and implement machine learning models, statistical analyses, and AI algorithms to enhance product functionality and performance. Take ownership of end-to-end model development including data preparation, model training, evaluation, and validation. Implement scalable and efficient data pipelines to support model development and deployment. Collaborate with software engineers and system teams to integrate machine learning models into production systems. Support model deployment, monitoring, and performance optimization in production environments. Participate in technical discussions and provide guidance to junior engineers. Collaborate with global teams to ensure consistency, reusability, and scalability of data science solutions. Implement validation and testing approaches to ensure model accuracy, robustness, and reliability. Quality Specific Goals: Aware of and comply with the GEHC Quality Manual, Quality Management System, Quality Management Policy, Quality Goals, and applicable laws and regulations as they apply to this job type/position. Complete all planned Quality & Compliance training within the defined deadlines. Identify and report any quality or compliance concerns and take immediate corrective action as required. Support compliance/closure of Regulatory and Quality requirements before completing Design Outputs/Program Deliverables. Participate in continuous improvement activities by identifying and appropriately escalating process and product quality gaps, providing solutions when possible.

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