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About This Role
Job ID R0121861 Date posted 10/21/2026
Why USAA?
At USAA, our mission is to empower our members to achieve financial security through highly competitive products, exceptional service and trusted advice. We seek to be the #1 choice for the military community and their families.
Embrace a fulfilling career at USAA, where our core values – honesty, integrity, loyalty and service – define how we treat each other and our members. Be part of what truly makes us special and impactful.
We are proud to support active-duty military spouses. USAA roles may offer remote or hybrid flexibility for active-duty military spouses consistent with applicable policy and business needs.
The Opportunity
USAA is seeking a Principal AI Platform Architect to lead the strategy, architecture, and evolution of our enterprise AI platform. This role will define scalable, secure, and compliant AI capabilities that enable generative AI, agentic AI, machine learning, and advanced analytics across the enterprise. Partnering with senior business and technology leaders, you will establish architecture standards, governance frameworks, and platform roadmaps that accelerate innovation, drive adoption, and deliver business value through responsible AI at scale.
We offer a flexible work environment that requires an individual to be in the office 4 days per week. This position can be based in one of the following locations: San Antonio, TX, Plano, TX, Charlotte, NC or Phoenix, AZ. Relocation assistance is available for this position.
What you'll do:
- Collaborates with business and IT stakeholders to understand and finalize strategy, requirements, measures, risks and controls across multiple domains considering cross stream impacts.
- Utilizes expertise and research to coordinate with domain architects in the creation of technology decisions with relevant architecture artifacts and proactively apply architecture thinking to conceive evolutionary strategies and roadmaps that can accelerate current capabilities across multiple business functions or IT domains.
- Cultivates productive and trusted relationships with front line business executives and IT stakeholders, to provide leadership and guidance to influence and shape priority, funding and execution of architecture solutions and direction for multiple domains and/or portfolios.
- Executes and assists in review of formal assessment of domain applications in collaboration with business and application owners to identify strengths, weaknesses, opportunities, threats and risks. Advises executive level business owners of proposed courses of action. Provides assessment summary content to the CTO for presentation to the board.
- Defines and documents architecture standards, principles, and policies for and across multiple domains to ensure architectural compliance of technology solutions. Use risk assessment methodologies to draft/evaluate and approve/reject waivers to architecture standards.
- Participates in activities that support the USAA technology ecosystem such as Program Increment planning and assistance in troubleshooting of Availability Command Center incidents to resolve complex production issues related to multiple technology domains.
- Provides professional and technical mentorship for experienced architects, engineers and IT community colleagues in an open, respectful, flexible and empathetic manner.
- Recommends mentoring opportunities specifically to create team redundancy and backfill ability.
- Leads the technical design and definition of systems, services, and dataflows across multiple domains and/or portfolios.
- Executes architecture practices and processes by ensuring timely completion of deliverables, driving and sponsoring enhancements.
- Maintains external relationships with contacts on industry organizations and government boards and vendors in the marketplace related to a business function or technology domain. Achieves one or more of the following external eminence activities: obtaining professional and industry technical certifications, presenting at conferences, publishing technical papers, contributing to open source, authoring patents, etc.
- Ensures risks associated with business activities are effectively identified, measured, monitored, and controlled in accordance with risk and compliance policies and procedures.
What you have:
- Bachelor's degree; OR 4 years of relevant education and/or experience.
- 10+ years of experience in technology architecture, platform engineering, software engineering, data architecture, or related disciplines, including leadership of enterprise-scale technology transformation initiatives.
- Demonstrated experience defining and evolving enterprise platform strategies, reference architectures, technology standards, and multi-year roadmaps that enable adoption of emerging technologies across a large organization.
- Experience architecting and implementing AI, machine learning, generative AI, or advanced analytics platforms, including governance, security, scalability, operationalization, and risk management considerations.
- Demonstrated success partnering across technology, product, data, risk, security, legal, and business organizations to establish operating models, governance structures, and execution frameworks that accelerate adoption and value realization.
- Deep understanding of how AI capabilities can be applied across the technology lifecycle, including architecture, software development, testing, operations, platform engineering, and developer productivity workflows.
- Demonstrated ability to evaluate, influence, and guide enterprise investments in AI technologies by defining measurable business outcomes, adoption strategies, value realization metrics, and success frameworks.
- Experience communicating complex technology strategies, architecture decisions, risks, and tradeoffs to both technical and non-technical audiences, including executive leadership.
- Demonstrated ability to influence organizational AI fluency through thought leadership, education, mentoring, architecture guidance, and cross-functional collaboration.
- Deep understanding of technology governance, architecture principles, security controls, regulatory considerations, and risk management practices within highly regulated industries.
- Experience leading cross-functional architecture efforts across application, data, integration, cloud, security, and AI domains to deliver cohesive enterprise technology capabilities.
What sets you apart:
- Experience architecting enterprise AI platforms leveraging technologies such as AWS Bedrock, OpenAI, Anthropic, or equivalent foundation model ecosystems.
- Experience designing and implementing enterprise agent architectures, orchestration frameworks, tool integration patterns, and autonomous workflow capabilities.
- Experience designing enterprise knowledge architecture and context management capabilities, including knowledge graphs, semantic models, vector search, metadata platforms, retrieval architectures, and context engineering patterns that enable scalable and trusted AI and agentic systems.
- Experience establishing enterprise AI governance, observability, evaluation, security, and model lifecycle management capabilities within highly regulated environments.
Compensation range: The salary range for this position is: $164,780.00 - $305,780.00.
USAA does not provide visa sponsorship for this role. Please do not apply for this role if at any time (now or in the future) you will need immigration support (i.e., H-1B, TN, STEM OPT Training Plans, etc.).
Compensation: USAA has an effective process for assessing market data and establishing ranges to ensure we remain competitive. You are paid within the salary range based on your experience and market data of the position. The actual salary for this role may vary by location.
Employees may be eligible for pay incentives based on overall corporate and individual performance and at the discretion of the USAA Board of Directors.
The above description reflects the details considered necessary to describe the principal functions of the job and should not be construed as a detailed description of all the work requirements that may be performed in the job.
Benefits: At USAA our employees enjoy best-in-class benefits to support their physical, financial, and emotional wellness. These benefits include comprehensive medical, dental and vision plans, 401(k), pension, life insurance, parental benefits, adoption assistance, paid time off program with paid holidays plus 16 paid volunteer hours, and various wellness programs. Additionally, our career path planning and continuing education assists employees with their professional goals.
For more details on our outstanding benefits, visit our benefits page on USAAjobs.com.
Applications for this position are accepted on an ongoing basis, this posting will remain open until the position is filled. Thus, interested candidates are encouraged to apply the same day they view this posting.
USAA 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, or status as a protected veteran.
Salary Context
This $164K-$305K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At USAA, 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 $212,040 based on 13,780 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($235K) sits 11% above the category median. Disclosed range: $164K to $305K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
USAA AI Hiring
USAA has 10 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, AI Product Manager. Positions span Plano, TX, Charlotte, NC, San Antonio, TX. Compensation range: $197K - $305K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 median, while Prompt Engineer roles sit at $140,000. 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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