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About This Role
At U.S. Bank, we’re on a journey to do our best. Helping the customers and businesses we serve to make better and smarter financial decisions and enabling the communities we support to grow and succeed. We believe it takes all of us to bring our shared ambition to life, and each person is unique in their potential. A career with U.S. Bank gives you a wide, ever\-growing range of opportunities to discover what makes you thrive at every stage of your career. Try new things, learn new skills and discover what you excel at—all from Day One.
Job Description
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U.S. Bank is seeking a Principal AI Research Scientist to join the Artificial Intelligence Center of Excellence (AI CoE), a high\-impact organization responsible for advancing the bank's AI strategy from early\-stage research through enterprise deployment.
This role is ideal for a highly technical and hands\-on AI leader who thrives at the intersection of architecture, engineering, innovation, and execution. The successful candidate will continuously evaluate emerging advances in artificial intelligence, identify opportunities to create business value, rapidly prototype novel solutions, and lead their evolution into scalable enterprise capabilities.
Unlike traditional research positions, this role spans the entire R\&D lifecycle. The ideal candidate combines deep scientific expertise with strong software engineering and solution delivery capabilities, enabling them to move seamlessly from research exploration and experimentation to deployment and adoption of production\-grade AI solutions.
The candidate is expected to be an active contributor to the broader AI community, maintain awareness of cutting\-edge academic and industry developments, and help shape the future direction of AI innovation within the bank.
Key Responsibilities
AI Research \& Innovation
- 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 executive stakeholders.
- Contribute to publications, patents, technical whitepapers, and thought leadership initiatives where appropriate.
Prototyping \& Solution Development
- 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.
- Contribute to architecture standards, reusable frameworks, reference implementations, technical whitepapers, and thought leadership initiatives where appropriate
Generative AI \& Agentic Systems
- Lead the development of 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.
- Establish 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.
Deployment \& Technical Leadership
- Partner with software engineers, platform teams, product managers, and business stakeholders to transition successful prototypes into production environments.
- Provide technical leadership throughout deployment and operationalization activities.
- Drive architectural decisions for AI solutions while balancing innovation, scalability, security, compliance, and operational requirements.
- Mentor engineers and data scientists and provide guidance on advanced AI techniques and best practices.
- Take ownership of outcomes and ensure successful delivery of AI capabilities from concept through adoption.
- Serve as the technical owner for AI platforms, frameworks, and shared services delivered by the team
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 Skills/Experience
- Master's degree or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Applied Mathematics, Statistics, or a related technical discipline.
- 8\+ years of experience in AI/ML research, development, and deployment.
- Strong programming skills in Python and modern AI/ML frameworks.
- Demonstrated experience designing, building, and deploying AI/ML solutions in production environments.
- Experience working across the full lifecycle of AI product development, from research and experimentation through deployment and operationalization.
- Deep expertise in machine learning, deep learning, neural networks, transformer architectures, and foundation models.
- Extensive 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.
- Strong hands\-on experience with PyTorch, Hugging Face, Langchain, LangGraph, or equivalent AI ecosystems.
- Demonstrated record of innovation through publications, patents, open\-source contributions, conference presentations, or significant 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 flexibility to work outside the office location for the other days.\*\**
If there’s anything we can do to accommodate a disability during any portion of the application or hiring process, please refer to our disability accommodations for applicants.
Benefits:
Our approach to benefits and total rewards considers our team members’ whole selves and what may be needed to thrive in and outside work. That's why our benefits are designed to help you and your family boost your health, protect your financial security and give you peace of mind. Our benefits include the following:
- Healthcare (medical, dental, vision)
- Basic term and optional term life insurance
- Short\-term and long\-term disability
- Pregnancy disability and parental leave
- 401(k) and employer\-funded retirement plan
- Paid vacation (from two to five weeks depending on salary grade and tenure)
- Up to 11 paid holiday opportunities
- Adoption assistance
- Sick and Safe Leave accruals of one hour for every 30 worked, up to 80 hours per calendar year unless otherwise provided by law
Review our full benefits available by employment status here.
U.S. Bank is an equal opportunity employer. We consider all qualified applicants without regard to race, religion, color, sex, national origin, age, sexual orientation, gender identity, disability or veteran status, and other factors protected under applicable law.
E\-Verify
U.S. Bank participates in the U.S. Department of Homeland Security E\-Verify program in all facilities located in the United States and certain U.S. territories. The E\-Verify program is an Internet\-based employment eligibility verification system operated by the U.S. Citizenship and Immigration Services.
The salary range reflects figures based on the primary location, which is listed first. The actual range for the role may differ based on the location of the role. In addition to salary, U.S. Bank offers a comprehensive benefits package, including incentive and recognition programs, equity stock purchase 401(k) contribution and pension (all benefits are subject to eligibility requirements). Pay Range: $139,230\.00 \- $163,800\.00
U.S. Bank will consider qualified applicants with arrest or conviction records for employment. U.S. Bank conducts background checks consistent with applicable local laws, including the Los Angeles County Fair Chance Ordinance and the California Fair Chance Act as well as the San Francisco Fair Chance Ordinance. U.S. Bank is subject to, and conducts background checks consistent with the requirements of Section 19 of the Federal Deposit Insurance Act (FDIA). In addition, certain positions may also be subject to the requirements of FINRA, NMLS registration, Reg Z, Reg G, OFAC, the NFA, the FCPA, the Bank Secrecy Act, the SAFE Act, and/or federal guidelines applicable to an agreement, such as those related to ethics, safety, or operational procedures.
Applicants must be able to comply with U.S. Bank policies and procedures including the Code of Ethics and Business Conduct and related workplace conduct and safety policies.
Posting may be closed earlier due to high volume of applicants.
Salary Context
This $139K-$163K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At U.S. Bank, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($151K) sits 29% below the category median. Disclosed range: $139K to $163K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
U.S. Bank AI Hiring
U.S. Bank has 10 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span Chicago, IL, US, Irving, TX, US, Saint Paul, MN, US. Compensation range: $115K - $200K.
Location Context
AI roles in San Francisco pay a median of $265,000 across 1,335 tracked positions. That's 23% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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