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About This Role
Director, Product Design \- AI in Experience DesignAbout the Job:
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The Experience Design (XD) team at Capital One is at the forefront of enriching our customers’ digital and physical experiences and we are passionate about creating memorable, meaningful product experiences that build the Capital One brand with humanity and drive business advantage and innovation. We champion a thriving environment of collaboration, authenticity and healthy critique, in which we honor diversity of thought, create a culture of belonging, and elevate one another. If you’re a creative innovator who embraces an environment where you can experiment, learn, and change banking for good, we’d love to hear from you.
Role Expectations
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The role is a part of our XD top of house team working closely with the SVP, Chief Design Officer and their Senior Leadership Team.
This Director will be responsible for building and owning the roadmaps across AI tooling, AI\-enabled design process evolution, and AI\-centric talent upskilling across Design at Capital One. With a destination\-backed mindset, this leader will play both a strategic role in defining the vision of how Design works with AI and also be a player\-coach role model for what it looks like to implement it, day\-to\-day. Operating at the intersection of high\-level strategy and hands\-on execution, you will partner directly with Senior Executives across the Product, Engineering, and Design landscape across the experimentation and adoption curve in AI, and focus on building a roadmap that balances bold, big\-picture thinking with immediate, actionable milestones.
The mission is twofold: to establish a definitive future vision for AI integration and to act as a catalyst for accelerating AI adoption across the Experience Design organization and beyond, balancing both risk and fast\-paced innovation. We are looking for a seasoned design veteran who carries the gravitas to influence the C\-suite, possesses the organizational rigor of a program lead, and maintains a deep, personal passion for AI experimentation—someone who isn't just watching the AI shift happen but is actively rewriting the how what the design discipline looks like in the future.
Here are some of the expected competencies for this role:
Technical Change Management: Drive transformative efficiency by architecting and scaling AI\-driven design processes and tooling—including Claude, Gemini, and assistive IDEs—across the organization's highly matrixed environment. Define required workforce transformation ensuring new technical capabilities are seamlessly integrated into the operating model. Partner with executive leadership to bridge the gap between high\-level innovation and disciplined execution to deliver measurable business value and experience excellence at scale
Product Design Expertise: Leverage established expertise and leadership in product design to elevate the quality and maturity of the design practice at Capital One. Leveraging a variety of skills across interaction, visual and service design; in collaboration with cross\-functional partners, help to oversee and optimize the use of appropriate tools, platforms, frameworks and design systems and develop artifacts that may include vision stories, journey maps, blueprints, high\-fidelity designs and prototypes
Strategic Influence: Shape product direction and outcomes by leveraging deep product design expertise, user insights, and an understanding of the business. Drive alignment with cross functional executive leaders, ensuring user needs are central to our overall strategy and enabling bold, innovative solutions to complex problems.
Human\-Centered: Recognize and pursue opportunities where customer needs and business goals coincide, while reinforcing with teammates and partners the importance of collecting and considering diverse customer perspectives throughout the design process
Business\-Focused: Demonstrate a mastery of products, processes, customers, competitors and broader industry trends and apply this knowledge to predict shifts in these areas and shape how Design should engage, uncover and solve significant business challenges
Problem\-Solving: Revel in the art of the possible, driving cross\-functional groups in thinking creatively about potential solutions, illuminating potential pitfalls and fostering shared learnings; build frameworks for complex decision\-making that enable effective debate and accelerate getting to the right solutions
Collaboration: Connect the dots across the organization, proactively bringing together executive partners in ways that drive more coherent experiences, accelerate delivery and elevate the value of design
Communication: Present work to executive leaders and stakeholders, modeling a strategic human\-centered approach that integrates design frameworks, data and research insights to make the complex accessible, influence change and drive strategic direction
The ideal candidate has a strong portfolio demonstrating your leadership, process, results and impact.
Basic Qualifications:
- At least 8 years of experience in a product design role
Preferred Qualifications:
- 10\+ years of experience in a product design role
- Experience leading a portfolio of work implementing AI processes and tools at scale in a design organization
- Has experience in a design toolkit that spans product design, service design, and content design
- Expertise with AI assistants tools like Claude or Gemini, agentic development like Claude Code, state machine visualizers like Stately, XState, and assistive tools like Windsurf
- Experience leading high\-impact, highly complex projects from strategy through delivery to market
- Experience partnering with product managers, engineers, marketing, and analytics leaders across multiple business lines to shape strategy, OKRs, roadmaps, and operating models
- Experience working within an established design system
- Proficiency with web and mobile technologies and platforms, navigating technology considerations and constraints to drive sound product and experience decisions
- Expertise with design and prototyping tools such as Figma
- Proficiency in business case development, grounding product and user experience decisions to business value
- Exceptional written and verbal communication skills
- Exceptional interpersonal skills and relationship building across complex organizations
- Exceptional people management and development skills
At this time, Capital One will not sponsor a new applicant for employment authorization for this position.
The minimum and maximum full\-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part\-time roles will be prorated based upon the agreed upon number of hours to be regularly worked.
McLean, VA: $230,400 \- $263,000 for Director, Design
New York, NY: $251,400 \- $286,900 for Director, Design
San Francisco, CA: $251,400 \- $286,900 for Director, Design
Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate’s offer letter.
This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan.
Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well\-being. Learn more at the Capital One Careers website. Eligibility varies based on full or part\-time status, exempt or non\-exempt status, and management level.
This role is expected to accept applications for a minimum of 5 business days.
No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non\-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug\-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23\-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901\-4920; New York City’s Fair Chance Act; Philadelphia’s Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries.
If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1\-800\-304\-9102 or via email at [email protected]. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.
For technical support or questions about Capital One's recruiting process, please send an email to [email protected]
Capital One does not provide, endorse nor guarantee and is not liable for third\-party products, services, educational tools or other information available through this site.
Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
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 2,799 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Capital One, 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 $175,000 based on 11,128 positions with disclosed compensation. Director-level AI roles across all categories have a median of $242,000.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $252,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,760; Mid: $159,385; Senior: $227,500; Director: $242,000; VP: $250,000.
Capital One AI Hiring
Capital One has 13 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, AI Software Engineer. Positions span McLean, VA, US, New York, NY, US, San Francisco, CA, US.
Location Context
Across all AI roles, 16% (460 positions) offer remote work, while 2,318 require on-site attendance. Top AI hiring metros: New York (2,241 roles, $208,300 median); San Francisco (1,822 roles, $252,000 median); Los Angeles (1,611 roles, $188,900 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 2,799 open positions tracked in our dataset. By seniority: 98 entry-level, 1,283 mid-level, 1,092 senior, and 326 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (460 positions). The remaining 2,318 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $252,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 30 roles); AI Safety ($274,200 median, 43 roles); Research Engineer ($260,000 median, 387 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 2,799 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (1,978), AI Software Engineer (197), Data Scientist (195). 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 (98) are outnumbered by mid-level (1,283) and senior (1,092) 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 326 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (460 positions), with 2,318 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 $200,000. Top-quartile roles start at $252,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,433 postings), Aws (840 postings), Rag (663 postings), Azure (639 postings), Gcp (537 postings), Pytorch (445 postings), Prompt Engineering (418 postings), Claude (396 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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