AI Engineer

GE HealthCare•Bellevue, NE

7d•Hybrid

About The Position

GE HealthCare is seeking a Senior Staff AI Engineer to design, build, deploy, and optimize production-grade AI solutions that power next-generation clinical applications. In this role, you will develop scalable AI systems using large language models (LLMs), foundation models, and modern machine learning technologies to automate clinical workflows while ensuring reliability, performance, and responsible AI deployment. At GE HealthCare, we are bringing AI- and cloud-based technologies to healthcare by delivering advanced analytics, visualization, multimodal learning, intelligent software, and scalable computing solutions across cloud and edge environments. Our Science & Technology organization develops AI capabilities that improve clinical workflows, enhance provider productivity, and help make healthcare more personalized, precise, and accessible.

Requirements

Master's degree in Science, Technology, Engineering, Mathematics (STEM), Computer Science, Artificial Intelligence, or a related technical field with 3+ years of relevant experience, or PhD in a STEM discipline with relevant experience developing production AI systems.
Demonstrated experience building and deploying large-scale Generative AI or foundation model solutions.
Experience developing applications using Large Language Models (LLMs), Agentic AI, or self-supervised learning techniques.
Strong understanding of modern machine learning techniques including transfer learning, generative models, optimization, and model evaluation.
Strong programming skills in Python and C++.
Experience developing scalable, maintainable, production-quality software.
Experience designing APIs, distributed services, or cloud-native AI applications.
Experience with modern AI frameworks such as PyTorch, Hugging Face, DeepSpeed, Megatron, or PyTorch Lightning.
Experience deploying AI workloads using MLOps, ModelOps, or Foundation Model Operations (FMOps) practices.
Experience working with large-scale model training or inference infrastructure.
Experience working with high-dimensional medical imaging, waveform, or time-series clinical datasets.

Nice To Haves

Experience solving complex engineering problems with ambiguous requirements.
Experience deploying large-scale distributed AI systems.
Experience with Spark, Hadoop, TensorFlow, or PyTorch in enterprise environments.
Experience building production data platforms and AI-powered software applications.
Track record of delivering machine learning solutions using large real-world healthcare datasets.
Experience optimizing large-scale AI training and inference performance.

Responsibilities

Design, develop, and deploy production-ready AI solutions using Large Language Models (LLMs), foundation models, and modern machine learning techniques to automate clinical workflows.
Build scalable AI applications leveraging electronic medical records (EMRs), medical waveforms, clinical reports, and other healthcare datasets.
Develop robust inference pipelines, model serving infrastructure, and AI services optimized for reliability, scalability, latency, and cost.
Optimize foundation models through prompt engineering, fine-tuning, distillation, quantization, and inference optimization techniques.
Implement responsible AI practices, including model evaluation, robustness testing, monitoring, and human-in-the-loop feedback mechanisms.
Collaborate with research scientists and cross-functional engineering teams to transition advanced AI models into production environments.
Build reusable software components, APIs, and development frameworks that enable scalable AI application development.
Stay current with emerging AI technologies, open-source frameworks, and industry best practices to continuously improve GE HealthCare's AI platform.