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About This Role
Posted Date
4/07/2026
Description
Sr. Director, Machine Learning Engineering (Remote\-Eligible)Overview:
At Capital One, we are creating responsible and reliable AI systems, changing banking for good. For years, Capital One has been an industry leader in using machine learning to create real\-time, personalized customer experiences. Our investments in technology infrastructure and world\-class talent — along with our deep experience in machine learning — position us to be at the forefront of enterprises leveraging AI. From informing customers about unusual charges to answering their questions in real time, our applications of AI \& ML are bringing humanity and simplicity to banking. We are committed to continuing to build world\-class applied science and engineering teams to deliver our industry leading capabilities with breakthrough product experiences and scalable, high\-performance AI infrastructure. At Capital One, you will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses who have come to love the products and services we build.
Team Description:
The Consumer Engagement Platform organization at Capital One empowers rapid financial product innovation at scale and delivers developer joy, for all Capital One’s consumer products and organizations, by providing well\-managed, self\-service, experimentation\-driven, and personalized product development vehicles. Hyper Personalization org is building the intelligence and infrastructure that will enable Capital One to deliver truly individualized, real\-time customer experiences at scale — turning every channel into a context\-aware decisioning surface, from home feeds to marketing and servicing messages. The org’s mission is to move Capital One to deliver always\-on, cohort\-of\-one personalization, powered by resilient data foundations, production\-grade ML and GenAI systems, and low\-latency application platforms that make it easy for teams across the company to experiment, innovate, and serve the right experience to every customer at the right moment.
What you’ll do in the role:
- Lead and scale a high\-performing engineering organization responsible for the Personalization Platform that powers real\-time, personalized product experiences and multi\-channel targeted user messaging across Capital One products and services.
- Define the technical strategy, delivery roadmap, and operating model for a portfolio spanning recommendation systems, ranking, decisioning, GenAI infrastructure, MLOps, and low\-latency application\-serving systems
- Build, develop, and manage engineers and engineering leaders; set a high bar for hiring, performance, talent density, coaching, and succession planning across the organization
- Partner cross\-functionally with Product, Data Science, Cloud Infrastructure, and Machine Learning Platform teams to align strategy, prioritize investments, and co\-develop advanced recommendation systems and algorithms serving Capital One users
- Drive the design, buildout, and operation of robust ML infrastructure and pipelines supporting feature extraction, model training, testing, guardrails, evaluation, deployment, and both real\-time and batch inference with strong reliability, scalability, and operational rigor
- Architect low\-latency, event\-driven systems for real\-time personalization and decisioning based on streaming data, user behavior, and contextual signals
- Drive the evolution of MLOps practices through automated, metrics\-backed deployment workflows, validation and testing systems, model lifecycle governance, and scalable observability
- Guide the adoption of state\-of\-the\-art AI and LLM optimization techniques to improve scalability, cost, latency, throughput, and reliability of large\-scale production AI systems
- Provide organizational technical and people leadership by influencing architecture, engineering standards, delivery excellence, incident management, and cross\-team strategy while mentoring managers, tech leads, and senior engineers.
- Make high judgment build\-vs\-buy decisions across a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more.
- Attract and retain top talent in the AI industry and nurture personal and professional development for your team. Foster a culture of learning and staying abreast of the state\-of\-the\-art in AI.
Capital One is open to hiring a Remote Employee for this opportunity.
Basic Qualifications:
- Bachelor's degree in Computer Science, Engineering, or AI plus at least 10 years of experience developing or leading AI and ML algorithms or technologies, or Master's degree plus at least 8 years of experience developing or leading AI and ML algorithms or technologies
- At least 5 years of people leadership experience
Preferred Qualifications:
- 7 years of experience managing and leading an engineering team
- 8\+ years of experience deploying scalable, responsible AI solutions on major cloud platforms (AWS, GCP, Azure)
- Master’s or PhD in Computer Science or a relevant technical field
Proven expertise designing, implementing, and scaling personalization platforms and recommendation systems across feed personalization, ads ranking, or targeted marketing messaging
- Proficiency in Python, Java, C\+\+, or Golang; hands\-on experience with ML frameworks (PyTorch, TensorFlow) and orchestration tools (Databricks, Airflow, Kubeflow)
- Experience optimizing large\-scale training and inference systems for hardware utilization, latency, throughput, and cost
- Deep expertise in cloud\-native engineering, containerization (Docker, Kubernetes), and automated CI/CD deployment
Deep experience with MLOps, model observability, and production ML lifecycle management
- Strong track record building organizations, developing managers and senior engineers, and leading through scale and ambiguity
Excellent communication and presentation skills, with the ability to influence senior stakeholders and articulate complex AI concepts clearly
- Proven leadership in driving platform strategy, cross\-functional execution, and technical direction across a large organization
- Excellent communication and presentation skills, with the ability to articulate complex AI concepts to peers
*Capital One will consider sponsoring a new qualified 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.
Remote (Regardless of Location): $286,200 \- $326,700 for Sr. Dir, Machine Learning Engineering
McLean, VA: $314,800 \- $359,300 for Sr. Dir, Machine Learning Engineering
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 RecruitingAccommodation@capitalone.com. 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 Careers@capitalone.com
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).
Sr. Director, Machine Learning Engineering (Remote\-Eligible)Overview:
At Capital One, we are creating responsible and reliable AI systems, changing banking for good. For years, Capital One has been an industry leader in using machine learning to create real\-time, personalized customer experiences. Our investments in technology infrastructure and world\-class talent — along with our deep experience in machine learning — position us to be at the forefront of enterprises leveraging AI. From informing customers about unusual charges to answering their questions in real time, our applications of AI \& ML are bringing humanity and simplicity to banking. We are committed to continuing to build world\-class applied science and engineering teams to deliver our industry leading capabilities with breakthrough product experiences and scalable, high\-performance AI infrastructure. At Capital One, you will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses who have come to love the products and services we build.
Team Description:
The Consumer Engagement Platform organization at Capital One empowers rapid financial product innovation at scale and delivers developer joy, for all Capital One’s consumer products and organizations, by providing well\-managed, self\-service, experimentation\-driven, and personalized product development vehicles. Hyper Personalization org is building the intelligence and infrastructure that will enable Capital One to deliver truly individualized, real\-time customer experiences at scale — turning every channel into a context\-aware decisioning surface, from home feeds to marketing and servicing messages. The org’s mission is to move Capital One to deliver always\-on, cohort\-of\-one personalization, powered by resilient data foundations, production\-grade ML and GenAI systems, and low\-latency application platforms that make it easy for teams across the company to experiment, innovate, and serve the right experience to every customer at the right moment.
What you’ll do in the role:
- Lead and scale a high\-performing engineering organization responsible for the Personalization Platform that powers real\-time, personalized product experiences and multi\-channel targeted user messaging across Capital One products and services.
- Define the technical strategy, delivery roadmap, and operating model for a portfolio spanning recommendation systems, ranking, decisioning, GenAI infrastructure, MLOps, and low\-latency application\-serving systems
- Build, develop, and manage engineers and engineering leaders; set a high bar for hiring, performance, talent density, coaching, and succession planning across the organization
- Partner cross\-functionally with Product, Data Science, Cloud Infrastructure, and Machine Learning Platform teams to align strategy, prioritize investments, and co\-develop advanced recommendation systems and algorithms serving Capital One users
- Drive the design, buildout, and operation of robust ML infrastructure and pipelines supporting feature extraction, model training, testing, guardrails, evaluation, deployment, and both real\-time and batch inference with strong reliability, scalability, and operational rigor
- Architect low\-latency, event\-driven systems for real\-time personalization and decisioning based on streaming data, user behavior, and contextual signals
- Drive the evolution of MLOps practices through automated, metrics\-backed deployment workflows, validation and testing systems, model lifecycle governance, and scalable observability
- Guide the adoption of state\-of\-the\-art AI and LLM optimization techniques to improve scalability, cost, latency, throughput, and reliability of large\-scale production AI systems
- Provide organizational technical and people leadership by influencing architecture, engineering standards, delivery excellence, incident management, and cross\-team strategy while mentoring managers, tech leads, and senior engineers.
- Make high judgment build\-vs\-buy decisions across a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more.
- Attract and retain top talent in the AI industry and nurture personal and professional development for your team. Foster a culture of learning and staying abreast of the state\-of\-the\-art in AI.
Capital One is open to hiring a Remote Employee for this opportunity.
Basic Qualifications:
- Bachelor's degree in Computer Science, Engineering, or AI plus at least 10 years of experience developing or leading AI and ML algorithms or technologies, or Master's degree plus at least 8 years of experience developing or leading AI and ML algorithms or technologies
- At least 5 years of people leadership experience
Preferred Qualifications:
- 7 years of experience managing and leading an engineering team
- 8\+ years of experience deploying scalable, responsible AI solutions on major cloud platforms (AWS, GCP, Azure)
- Master’s or PhD in Computer Science or a relevant technical field
Proven expertise designing, implementing, and scaling personalization platforms and recommendation systems across feed personalization, ads ranking, or targeted marketing messaging
- Proficiency in Python, Java, C\+\+, or Golang; hands\-on experience with ML frameworks (PyTorch, TensorFlow) and orchestration tools (Databricks, Airflow, Kubeflow)
- Experience optimizing large\-scale training and inference systems for hardware utilization, latency, throughput, and cost
- Deep expertise in cloud\-native engineering, containerization (Docker, Kubernetes), and automated CI/CD deployment
Deep experience with MLOps, model observability, and production ML lifecycle management
- Strong track record building organizations, developing managers and senior engineers, and leading through scale and ambiguity
Excellent communication and presentation skills, with the ability to influence senior stakeholders and articulate complex AI concepts clearly
- Proven leadership in driving platform strategy, cross\-functional execution, and technical direction across a large organization
- Excellent communication and presentation skills, with the ability to articulate complex AI concepts to peers
*Capital One will consider sponsoring a new qualified 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.
Remote (Regardless of Location): $286,200 \- $326,700 for Sr. Dir, Machine Learning Engineering
McLean, VA: $314,800 \- $359,300 for Sr. Dir, Machine Learning Engineering
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 RecruitingAccommodation@capitalone.com. 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 Careers@capitalone.com
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).
Salary
286,200\.00 \- 326,700\.00 Annual
Type
Full\-time
Salary Context
This $286K-$359K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Information Technology Senior Management Forum, 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 $166,983 based on 13,781 positions with disclosed compensation. Director-level AI roles across all categories have a median of $244,288. This role's midpoint ($322K) sits 93% above the category median. Disclosed range: $286K to $359K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Information Technology Senior Management Forum AI Hiring
Information Technology Senior Management Forum has 44 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, AI Engineering Manager, AI Software Engineer. Positions span McLean, VA, US, Irving, TX, US, Fort Worth, TX, US. Compensation range: $100K - $392K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% of all AI roles offer remote work.
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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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