Senior Staff AI Scientist

Job description

**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. **Job Description** **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. - Stay current with emerging AI advances and translate academic research and industry innovation into scalable enterprise solutions. - Publish research findings, contribute to patents, or create internal technical thought leadership that advances the organization’s AI maturity. - Build, fine-tune, and optimize ML/DL models, including supervised, unsupervised, reinforcement, and self-supervised learning systems. - Develop and deploy LLM-powered applications, conversational AI, summarization systems, semantic search, knowledge assistants, and intelligent automation platforms. - Create Generative AI applications using foundation models for text, image, code, synthetic data, and multimodal outputs. - Design and implement GAN-based solutions for synthetic data generation, image synthesis, anomaly simulation, data augmentation, and domain-specific generative use cases. - Develop Agentic AI systems capable of task planning, tool usage, workflow orchestration, memory integration, retrieval, and decision support. - Use AWS Bedrock to build and scale foundation model applications, including model access, orchestration, secure integration, and GenAI experimentation. - Use AWS SageMaker for model training, tuning, experimentation, MLOps, deployment, and monitoring at scale. - Work with structured and unstructured data across large-scale datasets to support AI research and production systems. - Lead or collaborate on data cleaning, feature engineering, data quality improvement, dataset curation, and annotation strategies. - Build robust AI pipelines that integrate with enterprise data systems, APIs, cloud services, and downstream applications. - Apply SQL, NoSQL, database modeling, and data warehousing concepts to support efficient model training and inference. - Partner with engineering teams to productionize models with scalability, observability, reliability, and security in mind. - Ensure all AI systems are designed and deployed with strong Responsible AI principles. - Develop practices for fairness, transparency, interpretability, explainability, privacy, accountability, and bias mitigation. - Assess risks associated with foundation models, LLM outputs, hallucinations, model drift, adversarial misuse, and unsafe automation. - Implement guardrails, evaluation standards, governance frameworks, and human-in-the-loop processes where necessary. - Support compliance with evolving data privacy, security, and ethical AI requirements. - Translate complex AI concepts into clear business value propositions for stakeholders, leadership teams, and non-technical audiences. - Collaborate with product, engineering, security, legal, data, and business teams to define AI strategy and deliver measurable outcomes. - Mentor junior scientists, ML engineers, and data professionals. - Contribute to roadmap planning, architecture reviews, technical hiring, and AI capability development across the organization. **Educational Qualifications** - PhD or master's in computer science, Artificial Intelligence, Machine Learning, NLP, Data Science, or a related quantitative discipline. **Required Qualifications** - Strong research background with demonstrated contributions in AI/ML through publications, patents, applied research, industrial innovation, or equivalent scientific work. - Deep knowledge of Machine Learning, Deep Learning, Natural Language Processing, Generative AI, Large Language Models, Agentic AI / AI Agents - Proven experience developing advanced AI models from research through implementation and evaluation. - Strong experience with AWS Bedrock and AWS SageMaker for foundation model development, model lifecycle management, and deployment workflows. - Strong understanding of Responsible AI, including model governance, fairness, explainability, privacy, bias mitigation, and risk control. **Core Technic