Capital One
Banking · Banking
- HQ
- McLean, US
- Founded
- 1994
- Website
- capitalone.com
Currently tracking 241 active AI roles, down 26% versus the prior 4 weeks. Primary focus: Serve · Engineering. Salary range $123k–$392k (avg $231k).
Banking · Banking
Capital One currently has 293 active AI-related job listings. The majority of these roles are focused on serving infrastructure, accounting for 28% of the total, followed closely by agents at 26% and post-training at 23%. Engineering is the dominant function, with 234 roles, and hiring is primarily concentrated in the United States. Frequent tech tags include model_serving, vector_db, and llm_observability, suggesting a focus on the operational aspects of AI deployment. In the last 30 days, Capital One posted 124 new AI roles, representing a 22% increase compared to the previous 30-day period.
Capital One currently has 305 active AI-related roles in our index. The most common open titles are: Senior Lead AI Engineer (AI Foundations, LLM Core and Agentic AI) (9), Lead AI Engineer (AI Foundations, LLM Core and Agentic AI) (8), Applied Researcher I (6), Distinguished Engineer (6), Applied Researcher II (5). Most positions are in Engineering and Research.
Capital One's active AI hiring is concentrated in: serving infrastructure (28%), agents (27%), post-training (23%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Capital One is hiring AI talent in: United States (299 roles), United Kingdom (3 roles), Canada (2 roles), Philippines (1 role).
Job postings at Capital One most frequently reference: model serving, vector db, fine tuning, llm observability, inference infra.
In the past 30 days, Capital One has posted 96 new AI-related roles. That is a -26% change versus the prior 30 days (130 → 96).
| Title | Stage | AI score |
|---|---|---|
| Senior Lead AI Engineer (FM Hosting, LLM Inference) Senior Lead AI Engineer focused on optimizing LLM inference performance, scalability, cost, and latency for production AI systems within Capital One's Intelligent Foundations and Experiences (IFX) team. The role involves designing, developing, and deploying AI software components, including foundation model training, inference, similarity search, guardrails, evaluation, and observability, leveraging cloud platforms and open-source AI technologies. | ServeAgent | 8 |
| Lead AI Engineer (FM Hosting, LLM Inference) Lead AI Engineer focused on optimizing LLM inference for scalability, cost, and latency within an enterprise AI setting. The role involves designing, developing, and deploying AI software components, including foundation model training, inference services, similarity search, guardrails, and model evaluation, leveraging cloud platforms and various AI technologies. | Serve |
| 8 |
| Lead AI Engineer Lead AI Engineer role focused on designing, developing, testing, deploying, and supporting AI software components including foundation model training, LLM inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability. The role involves leveraging AI technologies and optimizing LLM performance for scalability, cost, latency, and throughput within an enterprise AI context. The position emphasizes building AI-powered products and foundational AI systems for millions of customers. | ServeAgent | 8 |
| Senior Lead AI Engineer (AI Foundations, LLM Core and Agentic AI) Senior Lead AI Engineer focused on AI Foundations, LLM Core, and Agentic AI. The role involves designing, developing, testing, deploying, and supporting AI software components including foundation model training, LLM inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability. It also includes optimizing LLM performance for scalability, cost, latency, and throughput using various AI technologies and cloud platforms. The role emphasizes building responsible and reliable AI systems for banking applications. | ServeAgent | 8 |
| Lead AI Engineer (AI Foundations, LLM Core and Agentic AI) Lead AI Engineer focused on AI Foundations, LLM Core, and Agentic AI. The role involves designing, developing, testing, deploying, and supporting AI software components including foundation model training, LLM inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability. It also includes optimizing LLM performance for scalability, cost, latency, and throughput using various AI technologies and techniques. | ServeAgent | 8 |
| Senior Lead AI Engineer (AI Foundations, LLM Core and Agentic AI) Senior Lead AI Engineer focused on AI Foundations, LLM Core, and Agentic AI. The role involves designing, developing, testing, deploying, and supporting AI software components including foundation model training, LLM inference, similarity search, guardrails, model evaluation, and observability. It also emphasizes inventing and introducing LLM optimization techniques to improve the performance of large-scale production AI systems. | ServeAgent | 8 |
| Lead AI Engineer (AI Foundations, LLM Core and Agentic AI) Lead AI Engineer role focused on building and deploying AI-powered products, including foundation model training, LLM inference, similarity search, guardrails, and evaluation. The role involves optimizing performance, scalability, cost, and latency of large-scale production AI systems using various AI technologies and cloud platforms. | ServeAgent | 8 |
| Sr. Lead Machine Learning Engineer Senior Lead Machine Learning Engineer role focused on productionizing ML applications and systems at scale within a fintech company. Responsibilities include designing, building, and delivering ML models, informing ML infrastructure decisions, writing and testing code, automating tests and deployment, collaborating in Agile teams, retraining/maintaining/monitoring production models, leveraging cloud architectures, constructing data pipelines, and ensuring CI/CD best practices, code quality, risk governance, and Responsible AI. Requires experience in data-intensive solutions, programming, scaling ML systems, and leading ML development teams. | ServeData | 7 |
| Director, Data Science Director of Data Science at Capital One Canada, responsible for managing the risk and uncertainty in statistical models, leading the architecture and development of ML models through all phases, and leveraging technologies like Python and AWS, including agentic AI. The role involves managing talent and investigating new technologies for digital banking. | ServePost-train | 7 |
| Sr Lead Machine Learning Engineer This role focuses on productionizing and scaling machine learning applications and systems within a fintech domain. The engineer will be responsible for the design, development, deployment, and monitoring of ML models and infrastructure, with a strong emphasis on Python, Kubernetes, and cloud-based architectures. The role involves collaborating with data science and product teams, optimizing data pipelines, and ensuring the performance, availability, and responsible AI practices of ML systems. | ServeData | 7 |
| Sr. Lead Machine Learning Engineer Senior Lead Machine Learning Engineer responsible for productionizing ML applications and systems at scale, including designing, building, and delivering ML models and components, informing ML infrastructure decisions, writing and testing application code, automating tests and deployment, collaborating with cross-functional teams, retraining/maintaining/monitoring models in production, leveraging cloud architectures, constructing data pipelines, and ensuring CI/CD best practices, code management, risk governance, and Responsible AI. | ServeData | 7 |
| Sr. Lead Machine Learning Engineer Senior Lead Machine Learning Engineer responsible for productionizing ML applications and systems at scale, including designing, building, and delivering ML models and components, informing ML infrastructure decisions, writing and testing application code, automating tests and deployment, collaborating with cross-functional teams, retraining/maintaining/monitoring models in production, leveraging cloud architectures, constructing data pipelines, and ensuring CI/CD best practices, code management, risk governance, and Responsible AI. | ServeData | 7 |
| Senior Machine Learning Engineer (AI Foundations) Senior Machine Learning Engineer focused on building and productionizing ML models and components at scale within an enterprise AI context. The role involves designing, developing, and implementing ML applications, including LLMs and agentic systems, with a strong emphasis on infrastructure, operational efficiency, and responsible AI practices. | ServeData | 7 |
| Lead AI Engineer Lead AI Engineer role focused on designing, developing, and deploying AI-powered products and foundational AI systems. The role involves working with LLM inference, similarity search, guardrails, model evaluation, and optimization techniques to improve scalability, cost, and latency of production AI systems. It requires strong engineering and AI expertise, with a focus on building and scaling AI solutions within an enterprise context. | ServeAgent | 7 |
| Lead Machine Learning Engineer Lead Machine Learning Engineer at Capital One focused on productionizing ML applications and systems at scale. Responsibilities include designing, building, and delivering ML models, optimizing ML infrastructure, writing and testing code, automating tests and deployment, and maintaining/monitoring models in production. The role also involves constructing data pipelines and leveraging cloud-based architectures. | ServeData | 7 |
| Lead Software Engineer Lead Software Engineer for the MLX team, focused on building and deploying responsible GenAI and ML models, onboarding associates to GenAI/ML platforms, driving innovation, and integrating Generative AI/ML into the company's fabric. The role involves full-stack development, distributed microservices, observability, and AWS ML platform solutions. | ServeAgent | 7 |
| Distinguished Engineer Distinguished Engineer role focused on defining and delivering the next evolution of engineering within People Tech, shifting from domain-centric to a system-driven model. The role involves advancing Engineering and Operational Excellence, reducing duplication, and enabling faster, more reliable delivery through standardized execution and reuse. Responsibilities include articulating technical vision, decomposing complex problems, ensuring quality, serving as an expert on non-functional characteristics, mentoring, and architecting high-scale, reusable capabilities. Requires experience in designing and building distributed AI/ML systems and cloud computing. | Serve | 7 |
| Lead AI Engineer (AI Foundations, LLM Customization and Finetuning) Lead AI Engineer focused on AI Foundations, LLM Customization and Finetuning within Capital One's Intelligent Foundations and Experiences (IFX) team. The role involves designing, developing, testing, deploying, and supporting AI software components including foundation model training, LLM inference, similarity search, guardrails, model evaluation, governance, and observability. It requires leveraging AI technologies like AWS Ultraclusters, Huggingface, VectorDBs, and Nemo Guardrails, and inventing optimization techniques for performance, scalability, cost, latency, and throughput of large-scale production AI systems. The role also contributes to the technical vision and roadmap of foundational AI systems. | ServePost-train | 7 |
| Lead Machine Learning Engineer Lead Machine Learning Engineer at Capital One, focused on productionizing ML applications and systems at scale. Responsibilities include designing, building, and delivering ML models, developing ML infrastructure, writing and testing code, automating tests and deployment, and leveraging cloud-based architectures. The role emphasizes CI/CD, responsible AI, and optimized data pipelines for ML models. | Serve | 7 |
| Lead AI Engineer (AI Foundations, LLM Customization and Finetuning) Lead AI Engineer focused on AI Foundations, LLM Customization and Finetuning within Capital One's Intelligent Foundations and Experiences (IFX) team. The role involves designing, developing, testing, deploying, and supporting AI software components including foundation model training, LLM inference, similarity search, guardrails, model evaluation, governance, and observability. It requires leveraging AI technologies like AWS Ultraclusters, Huggingface, VectorDBs, and Nemo Guardrails, and inventing optimization techniques for performance, scalability, cost, latency, and throughput of large-scale production AI systems. The role also contributes to the technical vision and roadmap of foundational AI systems. | ServePost-train | 7 |
| Sr. Lead, Machine Learning Engineer (Enterprise Platforms Technology) Senior Machine Learning Engineer focused on building, scaling, and optimizing ML systems and applications within enterprise platforms. Responsibilities include designing, developing, and deploying ML models, constructing data pipelines, and ensuring high availability and performance of ML applications using cloud-based architectures and CI/CD best practices. | Serve | 7 |
| Distinguished Software Engineer - IFX This role is for a Distinguished Software Engineer focused on building and scaling the foundational compute infrastructure for an enterprise AI+ML platform. The engineer will work on distributed systems, cloud technologies, and support various AI/ML workloads including LLM pre-training, fine-tuning, inference, and agentic applications. | ServePretrain | 7 |
| Sr. Director, Cyber Technical (Cyber Hunt, Logging and Threat Detection) Senior Director role responsible for threat detection, cyber logging, privacy breach reporting, and threat hunting, with a focus on driving AI strategy for the cyber detection lifecycle and integrating AI/ML models for advanced threat detection and log management. | Serve | 7 |
| Lead Machine Learning Engineer Lead Machine Learning Engineer at Capital One focused on productionizing ML applications and systems at scale. Responsibilities include designing, building, and delivering ML models, informing ML infrastructure decisions, writing and testing application code, collaborating with Agile teams, retraining/maintaining/monitoring models in production, leveraging cloud architectures, constructing data pipelines, and ensuring CI/CD best practices. The role emphasizes building, scaling, and optimizing ML systems, with experience in Python/Scala/Java and distributed computing. | Serve | 7 |
| Manager, Data Science - AI for Data Manager of Data Science focused on AI for Data within a financial services company. The role involves partnering with cross-functional teams to deliver AI-enabled features across the data lifecycle, building machine learning models from design through implementation, and leveraging technologies like Python, Spark, and AWS. Experience with NLP, Information Retrieval, Search, Recommendations, and LLM fine-tuning is preferred. | ServePost-train | 7 |
| Lead Machine Learning Engineer (Enterprise Platforms Technology) Lead Machine Learning Engineer responsible for productionizing ML applications and systems at scale, including designing, building, and delivering ML models, optimizing ML infrastructure, developing data pipelines, and maintaining models in production within a cloud-based environment. The role emphasizes engineering best practices for ML systems. | ServeData | 7 |
| Lead Machine Learning Engineer (Enterprise Platforms Technology) Lead Machine Learning Engineer focused on productionizing ML applications and systems at scale within an enterprise environment. Responsibilities include designing, building, and delivering ML models, optimizing ML infrastructure, writing and testing application code, automating tests and deployment, and maintaining models in production using cloud-based architectures and CI/CD best practices. | Serve | 7 |
| Sr. Director, Product Management - AI/ML Platform Intelligence Sr. Director of Product Management to lead a team responsible for Capital One's Machine Learning and Generative AI platforms, focusing on core platform services for AI/ML that power business applications. Requires technical leadership, experience building products for data scientists and software developers, and familiarity with AI/ML infrastructure and tooling. | ServeAgent | 7 |
| Senior Associate, Data Science - Consumer Credit Risk Models and Data This role focuses on deploying, optimizing, and modernizing machine learning model pipelines and execution platforms for consumer credit risk management within a fintech company. The primary responsibility is to ensure these models are effectively implemented and provide insights for strategic decision-making, including loss allowances, stress testing, and capital allocation. | Serve | 7 |