Senior Staff AI Scientist

Job Description Summary

We are looking for an exceptional Sr Staff AI Scientist with a strong research background and deep expertise in Machine Learning, Deep Learning, GANs, NLP, Generative AI, LLMs, and Agentic AI. This role is ideal for a highly analytical and innovation-driven professional who can lead advanced AI research, design production-grade intelligent systems, and translate emerging AI capabilities into real business impact.

The ideal candidate will hold a PhD or Masters in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Computational Linguistics, Applied Mathematics, Statistics, or a related field, with proven experience in both scientific research and practical AI solution development. The candidate should also have hands-on expertise with AWS Bedrock, AWS SageMaker, and Responsible AI practices, including fairness, explainability, governance, privacy, and bias mitigation.

This role requires a rare blend of scientific depth, engineering strength, business understanding, and the ability to work across highly ambiguous and fast-evolving AI problem spaces.

GE Healthcare is a leading global medical technology and digital solutions innovator. Our mission is to improve lives in the moments that matter. Unlock your ambition, turn ideas into world-changing realities, and join an organization where every voice makes a difference, and every difference builds a healthier world.

Key Responsibilities:

• Conduct advanced research in artificial intelligence, with focus areas including machine learning, deep learning, generative AI, large language models, natural language processing, GANs, multimodal AI, and agentic AI systems.

• Design, prototype, and validate novel AI algorithms, architectures, and workflows for real-world use cases.

• Explore and apply cutting-edge approaches in transformers, fine-tuning, retrieval-augmented generation (RAG), prompt optimization, autonomous agents, multi-agent systems, model alignment, and reasoning frameworks.

• Lead experimentation across model training, evaluation, benchmarking, and optimization.