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About This Role
About this role:
Wells Fargo is seeking an experienced Quantitative Analytics Senior Manager to join the Frontier AI Model Methodology team. The Frontier AI Model Methodology team plays a critical role in developing and productionizing methodologies, AI agents, and systems that transform and accelerate model development and validation at Wells Fargo scale. One of its key functions is to integrate, in close collaboration with Model Risk Officers, these advanced methodologies and AI agents into the overall risk management strategy, ensuring consistency and alignment with enterprise-wide objectives. This synergy enhances the robustness and reliability of models leveraged by the company, strengthens the effectiveness of model validations, and contributes to the continuous improvement of modeling standards and practices across the enterprise.
In this role you will manage, lead and build our Enterprise Model Validation platform, AI agents, and team. You will be responsible for driving automated validation and monitoring of models, while building strategic partnerships with stakeholders including Lines of businesses, technology and model risk management. This role will report to the Head of Frontier AI Model Methodology.
In this role, you will:
- Drive the development and delivery of industry-leading, AI agentic-based model validation, testing, and real-time monitoring capabilities
- Provide leadership and technical expertise in designing and delivering AI agentic solutions and systems on cloud-native environment such as Google Cloud Platform (GCP), enabling real‑time processing and large‑scale distributed systems.
- Define AI Product requirements and manage the agentic product lifecycle to ensure successful delivery.
- Lead cross‑functional collaboration with Technology partners to unlock synergies that accelerate delivery and strengthen execution.
- Demonstrate hands‑on leadership by directly contributing to design, development, and execution of solutions alongside teams.
- Partner with the Model Risk and Model Development teams to design and build scalable platforms and agent-based solutions that accelerate real-time model validation, testing and monitoring.
- Develop and guide a culture of talent development to meet business objectives and strategy.
- Manage a team of highly skilled AI scientists and engineers.
Required Qualifications:
- 7+ years of Quantitative Analytical experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
- 3+ years of management or leadership experience
- Master's degree or higher in a quantitative discipline such as mathematics, statistics, engineering, physics, economics, or computer science
Desired Qualifications:
- Prior experience as a hands-on technical leader building Agentic AI systems leveraging GenAI (including Large Language and Reasoning Models)
- Demonstrated ability to apply GenAI / Agentic AI in the full build lifecycle - design, implementation, testing, and iteration - leveraging AI coding copilots (e.g., GitHub Copilot) and agentic coding workflows to accelerate delivery while maintaining enterprise standards
- Prior experience in AI / ML (including Agentic AI, GenAI, ML) model evaluation, validation, real-time model monitoring
- Experience in Product Development, including developing product roadmaps and go to market plans
- Proven track record in delivering in fast-past environment
- Proven track record in leading high performing teams
- Proven experience leading and managing teams in Agile development environments (e.g., Scrum, Kanban), including sprint planning, delivery oversight, and continuous improvement.
- Excellent leadership and communication skills with the ability to build effective partnerships with stakeholders
- Strong domain expertise in financial services in delivering AI/ML and emerging AI pilots and aligning product, product value, and platform roadmaps.
- Strong understanding of current and emerging technology landscape (including Agentic AI) supporting the delivery of new generation of model validation system
- Ability to build excellent partnerships with model developers, validators, users, audit, and governance partners
- Success in creating a culture of continuous improvement in efficiency and effectiveness within the team
- Prior experience in financial services or related industry.
Job Expectations:
- This position offers a hybrid work schedule
- Willingness to work onsite at stated location on the job posting
- This position is eligible for VISA sponsorship
Job Posting Locations:
- Three Wells Fargo Center, CHARLOTTE, NC
- 600 Wells Fargo, MINNEAPOLIS, MN
- Las Colinas Bldg B, Irving Campus, IRVING, TX
- West Des Moines Campus, 800 S Jordan Creek Pkwy, WEST DES MOINES, IA
- D Building,114N Beaumont St -D Bldg., SAINT LOUIS, MO
- 1150 W Washington, TEMPE, AZ
- 333 Market, SAN FRANCISCO, CA
Pay Range
Reflected is the base pay range offered for this position. Pay may vary depending on factors including but not limited to demonstrated examples of prior performance, skills, experience, or work location. Employees may also be eligible for incentive opportunities. $185,000.00 - $300,000.00Benefits
Wells Fargo provides eligible employees with a comprehensive set of benefits, many of which are listed below. Visit Benefits - Wells Fargo Jobs for an overview of the following benefit plans and programs offered to employees.
- Health benefits
- 401(k) Plan
- Paid time off
- Disability benefits
- Life insurance, critical illness insurance, and accident insurance
- Parental leave
- Critical caregiving leave
- Discounts and savings
- Commuter benefits
- Tuition reimbursement
- Scholarships for dependent children
- Adoption reimbursement
Posting End Date:
18 Oct 2026* Job posting may come down early due to volume of applicants.
We Value Equal Opportunity
Wells Fargo is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other legally protected characteristic.
Employees support our focus on building strong customer relationships balanced with a strong risk mitigating and compliance-driven culture which firmly establishes those disciplines as critical to the success of our customers and company. They are accountable for execution of all applicable risk programs (Credit, Market, Financial Crimes, Operational, Regulatory Compliance), which includes effectively following and adhering to applicable Wells Fargo policies and procedures, appropriately fulfilling risk and compliance obligations, timely and effective escalation and remediation of issues, and making sound risk decisions. There is emphasis on proactive monitoring, governance, risk identification and escalation, as well as making sound risk decisions commensurate with the business unit’s risk appetite and all risk and compliance program requirements.
Applicants with Disabilities
To request a medical accommodation during the application or interview process, visit Disability Inclusion at Wells Fargo.
Drug and Alcohol Policy
Wells Fargo maintains a drug free workplace. Please see our Drug and Alcohol Policy to learn more.
Wells Fargo Recruitment and Hiring Requirements:
a. Third-Party recordings are prohibited unless authorized by Wells Fargo.
b. Wells Fargo requires you to directly represent your own experiences during the recruiting and hiring process.
Salary Context
This $185K-$300K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Wells Fargo, 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($242K) sits 14% above the category median. Disclosed range: $185K to $300K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Wells Fargo AI Hiring
Wells Fargo has 11 open AI roles right now. They're hiring across AI/ML Engineer, Research Scientist, AI Engineering Manager, AI Software Engineer. Positions span West Des Moines, IA, US, St. Louis, MO, US, Charlotte, NC, US. Compensation range: $260K - $305K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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