AI Research Scientist

AI Scientist

U.S. Bank is seeking an AI Scientist to join the Artificial Intelligence Center of Excellence (AI CoE), a high-impact organization responsible for advancing the bank's AI strategy through applied research, innovation, rapid prototyping, and enterprise deployment.

This role is ideal for a highly technical and hands-on AI practitioner who thrives at the intersection of research, experimentation, and implementation. The successful candidate will continuously evaluate emerging advances in artificial intelligence, identify opportunities to create business value, rapidly prototype novel solutions, and help evolve successful concepts into scalable enterprise capabilities.

Unlike traditional data science roles, this position spans the full AI research and development lifecycle. The ideal candidate combines strong scientific and engineering expertise with a practical mindset, enabling them to move seamlessly from exploratory research and experimentation to the development and deployment of production-grade AI solutions.

The candidate is expected to remain actively engaged with the broader AI community, stay current on academic and industry developments, and contribute to advancing AI innovation within the bank.

• Monitor, evaluate, and experiment with emerging AI technologies, research breakthroughs, and industry trends, with particular emphasis on generative AI, large language models (LLMs), multimodal AI, and agentic AI systems.
• Identify opportunities to apply advanced AI techniques to complex business problems across financial services.
• Conduct original AI research and exploratory investigations to assess the feasibility and value of novel approaches.
• Develop hypotheses, design experiments, evaluate results, and communicate findings to technical and business stakeholders.
• Contribute to patents, technical publications, whitepapers, prototypes, and innovation initiatives.

• Rapidly transform research concepts into working prototypes and proof-of-concepts.
• Design, develop, and validate AI solutions across the full lifecycle, from data preparation and modeling through deployment and monitoring.
• Build experimental and production-ready solutions using modern AI/ML tools, frameworks, and cloud platforms.
• Apply best practices in model evaluation, benchmarking, explainability, security, and responsible AI.

• Develop solutions leveraging foundation models, generative AI, retrieval-augmented generation (RAG), fine-tuning techniques, and agentic workflows.
• Design and evaluate AI agents, multi-agent systems, orchestration frameworks, tool-use architectures, memory systems, and human-in-the-loop workflows.
• Contribute to best practices for prompt engineering, model adaptation, evaluation, observability, and governance of LLM-based systems.
• Assess emerging model architectures and determine their applicability within a highly regulated environment.

• Partner with software engineers, platform teams, product managers, and business stakeholders to transition successful prototypes into production environments.
• Contribute to deployment and operationalization efforts for AI solutions.
• Help balance innovation, scalability, security, compliance, and operational requirements throughout solution development.
• Share knowledge and provide technical guidance to peers and junior team members.
• Take ownership of assigned initiatives and ensure successful delivery of AI capabilities from concept through deployment.

Basic Qualifications

• Bachelor's degree in a quantitative field such as statistics, computer science, engineering or applied mathematics, or equivalent work experience
• Eight or more years of relevant experience

Preferred Qualifications

• Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Applied Mathematics, Statistics, Computational Linguistics, or a related field strongly preferred.
• Deep expertise in machine learning, deep learning, neural networks, transformer architectures, and foundation models.
• Experience with generative AI technologies, including LLMs, fine-tuning, RAG systems, model evaluation, and inference optimization.
• Experience building and evaluating agentic AI systems, multi-agent workflows, AI orchestration frameworks, and autonomous decision-making architectures.
• Hands-on experience with PyTorch, Hugging Face, Lang Chain, Lang Graph, or equivalent AI ecosystems.
• Demonstrated record of innovation through publications, patents, open-source contributions, conference presentations, or impactful AI solution delivery.
• Experience developing AI solutions within highly regulated industries such as financial services, healthcare, insurance, or telecommunications.
• Strong communication skills and the ability to translate complex AI concepts into actionable business outcomes.

** The role offers a hybrid/flexible schedule, which means there's an in-office expectation of 3 or more days per week and the…