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About This Role
Navy Federal Credit Union currently does not provide sponsorship for this role. Applicants must be authorized to work in the United States without the need for current or future sponsorship.
Navy Federal's Internal Audit team is in the midst of an exciting transformational journey to become a best-in-class Audit function! It is our vision to be a preferred advisor to the business by building and cultivating trust through the consistent execution of high-quality and risk-focused audit and advisory work. We’re focused on implementing efficient processes, maximizing our use of technology, integrating data analytics into everything we do, and investing in our biggest asset, our people. If this sounds like the type of team you’d like to be a part of, then we want to learn more about you!
Provide independent assurance over data science, machine learning, generative AI, and agentic AI capabilities through audit engagements and technical reviews. Evaluate model, AI and data governance frameworks, lifecycle controls, and oversight effectiveness, including risks associated with model complexity, uncertainty, and operational performance. Lead quantitative analyses that inform risk management activities and serve as a subject matter expert in model design, data-driven experimentation, and scalable analytical solutions. Advise Internal Audit staff, senior management, and business partners on AI/model lifecycle management, data governance, regulatory expectations, and industry practices. Conduct and manage increasingly complex projects with moderate supervision and independent judgment.
This position is eligible for the TalentQuest employee referral program. If an employee referred you for this job, please apply using the system-generated link that was sent to you.
Responsibilities
- Leads IA’s assessment of the design, development, validation, and implementation of advanced quantitative models, analytics strategies and AI products.
- Ability to evaluate approaches to assess model performance, scalability, robustness, bias, sensitivity, and risk under varying assumptions or operating conditions across model and AI products.
- Partners with cross functional teams to assess management’s alignment of quantitative solutions with business objectives, technical feasibility, and implementation priorities.
- Mentors junior team members on modeling techniques, coding practices, documentation standards, and analytical storytelling.
- Develops and advances reusable frameworks, technical standards, and best practices that improve the quality and efficiency of IA quantitative analytics work.
- Communicates complex concepts, tradeoffs, and recommendations to senior stakeholders in a clear, practical, and decision-oriented manner.
- Identifies high value analytical opportunities and defines appropriate methodologies to address complex, ambiguous problems.
Qualifications
- Bachelor’s degree in analytics, mathematics, statistics, computer science, data science, operations research, economics, finance or the equivalent combination of education, training or experience; Master's/advanced Degree preferred.
- 5+ years of experience in analytics, mathematics, statistics, computer science, data science, operations research, economics, finance.
- Complete knowledge and understanding of business area/specialization.
- Advanced knowledge of quantitative modeling, optimization, statistical inference, machine learning, simulation, or algorithm design.
- Strong programming skills and experience building production quality analytical tools, data pipelines, models, or decision support systems.
- Demonstrated ability to lead complex analyses from problem definition through implementation and stakeholder adoption.
- Strong communication, influence, and consultative problem-solving skills.
Desired Qualifications:
- Prior experience independently assessing and challenging model risk management, data governance and AI/Agentic governance within risk management or internal audit environments.
- Understanding of various models and modeling practices used in credit risk management, fraud detection, BSA/AML, operations, treasury & finance, marketing models, etc.
- Deep knowledge and experience with model risk regulatory guidance; e.g., SR 11-7, SR 26-2, ASOP 56, as well as developing AI frameworks including NIST AI Risk Management Framework, and ISO/IEC 42001Artificial Intelligence Management System.
- Knowledge of one or more regulations and frameworks such as CECL, CCAR, BSA/Anti-Money Laundering, ECOA, FCRA, etc.
- Understanding of various models and modeling practices used in credit risk management, fraud detection, BSA/AML, operations, treasury & finance, marketing models, etc.
- Programming, data modeling, simulation, and advanced mathematics
- Familiarity with coding languages such as SQL, R, Python, Hadoop, or SAS.
- Knowledge of AI platforms and data ecosystems supporting machine learning, generative AI, analytics, and LLM-enabled systems, including Microsoft Copilot Studio, Azure AI Foundry, AWS, Databricks and PowerBI.
- Master's Degree in Data Science, Statistics, Mathematics, Computers Science, Engineering, or another quantitative or related field.
Additional Information
Hours:
- Monday - Friday, 8:00AM - 4:30PM
Locations:
- 820 Follin Ln. Vienna, Virginia 22180
- 141 Security Drive Winchester, VA 22602
- 5550 Heritage Oaks Dr. Pensacola, FL 32526
About Us
Navy Federal provides much more than a job. We provide a meaningful career experience, including a culture that is energized, engaged and committed; and fierce appreciation for our teams, who are rewarded with highly competitive pay and generous benefits and perks.
Our approach to careers is simple yet powerful: Make our mission your passion.
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From Fortune . ©2025 Fortune Media IP Limited. All rights reserved. Used under license. Fortune and Fortune Media IP Limited are not affiliated with, and do not endorse products or services of, Navy Federal Credit Union.
Equal Employment Opportunity: All qualified applicants will receive consideration for employment without regard to age, race, sex, color, religion, national origin, disability, veteran status, pregnancy, sexual orientation, genetic information, gender identity or any other basis protected by applicable law.
Accommodations: If you need accommodation or assistance for a qualifying condition to complete the online application (or during any stage of the hiring process), you can contact Navy Federal's Medical Accommodations team at [email protected] or by calling 1-888-503-6013. This team cannot provide any information on job postings or application status.
Disclaimers: Navy Federal reserves the right to fill this role at a higher/lower grade level based on business need. An assessment may be required to compete for this position. Job postings are subject to close early or extend out longer than the anticipated closing date at the hiring team’s discretion based on qualified applicant volume. Navy Federal Credit Union assesses market data to establish salary ranges that enable us to remain competitive. You are paid within the salary range, based on your experience, location and market position. For additional details regarding compensation and benefits, review the Benefits page of the Navy Federal Career Site.
Protect Yourself from Job Scams: Navy Federal Credit Union jobs are posted on our career site, jobs.navyfederal.org and reputable job boards (e.g., LinkedIn, Indeed). We do not post jobs on social media marketplaces, messaging apps or unverified websites. We will never ask candidates for payment, bank details or personal financial information during the hiring process.
Bank Secrecy Act: Remains cognizant of and adheres to Navy Federal policies and procedures, and regulations pertaining to the Bank Secrecy Act.
Salary Context
This $115K-$181K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Navy Federal Credit Union, 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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($148K) sits 30% below the category median. Disclosed range: $115K to $181K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Navy Federal Credit Union AI Hiring
Navy Federal Credit Union has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Pensacola, FL, US. Compensation range: $181K - $181K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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