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About This Role
Director, Information Security Office AI/MLAt Capital One, the AIML Division is working to bring the transformative power of emerging AI capabilities, to reimagine how we serve our customers and businesses. We are looking for an experienced Director ISO to support the AIML Division objective to Accelerate AI Innovation and build and maintain Enterprise\-grade Agentic Capabilities.you will help consult on initiatives, programs, and projects to raise their game in Information Security and Risk Management. You are pragmatic and practical in your understanding of risk and security, but also willing to lean into solutions or know when to pull in additional experts and or escalate. You collaborate and innovate with other teams within Capital One to push the envelope. You are comfortable with technologies like Cloud services, Containers, Docker, Microservices, Serverless, APIs, DevOps, micro\-segmentation, Customer Servicing platforms and technologies security, generative AI security, PCI and other business and regulatory cybersecurity compliance frameworks. Security is essential to what we do here, from protecting our customers to our associates.
Responsibilities:
- Serve as the subject matter expert guiding projects and technical investigative teams to protect a large enterprise
- Strategize and implement technology best practices for risk mitigation associated with analysis of security operations center alerts and cases
- Engage with internal teams and cyber/technology industry partners to ensure threats from external actors are evaluated and mitigated where appropriate
- Understand the business drivers, and associate culture of the enterprise and partner with relevant stakeholders to ensure regulatory compliance, data protection, and associate protection are are in place
- Collaborate with engineering and other teams to drive automation for analysis efficiency, containment, and remediation activities
- Advocate for change, and excel at partnering with various cross functional stakeholders
- Collaborate with other CSOC leaders to deliver a continuous monitoring and response capability
- Use data across CSOC operations to drive decisions, with emphasis on resource allocation, threat mitigation, and associate development
- Serve as the technical advisor for the Senior Director
Skills:
- Subject matter expertise on Cyber Security Operations Centers including SIEM, SOAR, and Investigative methods
- Experience with monitoring and protection tailored to specific applications
- Understanding of the System Development Lifecycle and influencing developers
- Must be able to work with little oversight, representing the company’s interests and values
- Outstanding customer service and stakeholder management
- Experience with security operations center, data analysis, and security event and incident management
- Must be able to apply independent judgment
- Strong ability compartmentalize information and structure findings in an easy to consume format
- Thought leadership and industry influence
Basic Qualifications:
- Bachelor's Degree
- At least 7 years of experience in Cybersecurity or information technology
- At least 5 years of experience in Application Development
- At least 5 years of experience with Linux, Unix, and Windows operating systems
Preferred Qualifications:
- 3\+ years experience managing security operations projects
- 3\+ years experience optimizing security operations tooling
- 2\+ years experience securing customer servicing agents platforms
- 2\+ years experience in securing Generative AI platforms and applications
- 2\+ years of experience managing, monitoring, or configuring public cloud environments (AWS, Azure, GCP)
- Professional Security Certifications ( CISSP, CISM, CCSP, Security\+, CEH, SANS GIAC 503 or 504, AWS Security)
*At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (i.e. H1B, F\-1 OPT, F\-1 STEM OPT, F\-1 CPT, J\-1, TN, E\-2, E\-3, L\-1 and O\-1, or any EADs or other forms of work authorization that require immigration support from an employer).*
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: $269,100 \- $307,200 for Director, Cyber Technical
Plano, TX: $244,700 \- $279,200 for Director, Cyber Technical
Richmond, VA: $244,700 \- $279,200 for Director, Cyber Technical
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Capital One AI Hiring
Capital One has 32 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Engineering Manager, AI Software Engineer. Positions span McLean, VA, US, New York, NY, US, San Jose, CA, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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