Senior Staff AI Scientist

Job description

Job Description SummaryWe 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 DescriptionKey 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 Technical Skills:Expert-level proficiency in Python as the top priority language for AI and ML development. Ability to build efficient, scalable, and production-ready code for research and enterprise AI applications. Strong understanding of core ML concepts, including Transformer architectures Hands-on experience with leading frameworks such as PyTorch, TensorFlow, Keras Experience with model selection, hyperparameter tuning, training optimization, evaluation metrics, model compression, and inference performance improvement. Strong expertise in NLP techniques, including text classification, NER, embeddings, summarization, semantic retrieval, question answering, sentiment analysis, and conversational AI. Experience building LLM applications, including prompt engineering, fine-tuning, RAG pipelines, evaluation, grounding, and safety controls. Expertise in Generative AI architectures, foundation models, and enterprise use cases involving text, image, document, and multimodal generation. Strong experience building AI agents and autonomous workflows. Skills in, Agent architecture and orchestration, Tool use and function calling, Retrieval systems, Memory design, Reliability engineering, Evaluation and guardrails, Multi-step planning and execution Familiarity with modern agent frameworks and orchestration patterns for enterprise-grade agentic systems. Experience in Data cleaning and preprocessing, Feature engineering, SQL and database querying, Database modeling, NoSQL systems, Data warehousing, Large-scale data handling Ability to work with diverse datasets and establish strong data foundations for AI systems. Ability to apply mathematical reasoning to model design, tuning, experimentation, and performance analysis. Strong experience with AWS Bedrock, AWS SageMaker, AWS data and ML services relevant to AI model development and deployment Familiarity with cloud-native AI system design, scalable training, model serving, monitoring, and MLOps practices. Strong commitment to designing fair, accountable, transparent, and human-centered AI systems. Ability to identify, assess, and mitigate ethical