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.
Currently tracking 241 active AI roles, down 26% versus the prior 4 weeks. Primary focus: Serve · Engineering. Salary range $123k–$392k (avg $231k).
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 |
|---|---|---|
| Principal Associate, Data Scientist - People Strategy & Analytics Data Scientist role focused on applying AI/ML, specifically LLMs with RAG and prompt engineering, to HR talent decisions. The role involves building NLP and ML models through all development phases, partnering with cross-functional teams, and leveraging technologies like Python, SQL, AWS, LangChain, Hugging Face, VectorDBs, and Pytorch/TensorFlow. The ideal candidate is innovative, creative, technical, and statistically-minded. | AgentPost-train | 7 |
| Senior Associate, Data Scientist - People Strategy & Analytics Senior Associate, Data Scientist role focused on People Strategy & Analytics, applying AI/ML to talent decisions. Responsibilities include developing NLP and ML models, using LLMs with RAG and evaluation frameworks, partnering with cross-functional teams, and leveraging technologies like Python, SQL, AWS, LangChain, Hugging Face, and VectorDBs. The ideal candidate is innovative, creative, technical, and statistically-minded. |
| AgentPost-train |
| 7 |
| Senior Manager, Data Science, Bank Operations Senior Manager, Data Science for Bank Operations at Capital One. This role involves building machine learning models for core internal capabilities like Check/Document Reading, Anomaly Identification, NLP of Calls, and operational forecasting, utilizing technologies such as neural networks, LLMs, and transformer architectures. The candidate will partner with cross-functional teams, leverage technologies like Python, AWS, H2O, and Spark, and manage the full lifecycle of ML model development. | Post-train | 7 |
| Principal Associate, Data Scientist - Bank Operations Data Science The Bank Operations Data Science team builds machine learning models for internal capabilities like Check/Document Reading, Anomaly Identification, Natural Language Processing of Calls, and operational forecasting, using technologies like neural networks, LLMs, and transformer architectures. The role involves partnering with cross-functional teams, leveraging technologies like Python and Snowflake, and building ML models through all phases of development. The ideal candidate is innovative, creative, technical, and statistically-minded, with experience in various ML techniques and cloud platforms. Specific experience with LLM risk mitigation is preferred. | Post-train | 7 |
| 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, retraining/maintaining/monitoring production models, leveraging cloud architectures, constructing data pipelines, and ensuring responsible AI practices. The role emphasizes scaling ML solutions and integrating them into production environments. | ShipServe | 7 |
| 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 |
| Data Analyst Manager - Model Risk Office Manager for a Data Analyst team focused on automating model risk management processes using AI and ML, including building conversational chatbots and multi-agent systems. The role involves optimizing data integration and driving strategy through analytics, with a strong emphasis on AI-powered automation. | AgentServe | 7 |
| Manager, Data Scientist - Partnerships Acquisitions This role is for a Manager, Data Scientist focused on Partnerships Acquisitions within Capital One's fintech domain. The primary responsibility is to lead the development of next-generation machine learning models for credit decisioning, including application approval/decline, product optimization, and customer valuation. The role involves partnering with cross-functional teams, leveraging technologies like Python, AWS, and Spark, and building models through all phases of development. The ideal candidate has a strong technical and statistical background with hands-on experience in data science solutions and machine learning. | Post-train | 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 |
| Principal Associate, Data Scientist - Partnerships Acquisitions Capital One is seeking a Principal Associate Data Scientist to join their Partnerships Acquisitions team. This role focuses on building and implementing machine learning models for credit decisioning, product optimization, and customer valuation within the fintech domain. The position requires a strong background in data science, machine learning, and statistical modeling, with experience in Python, AWS, and SQL. The candidate will work with a cross-functional team to deliver data-driven products and enhance decision accuracy and efficiency. | Post-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 |
| Principal Associate, Data Scientist - Cash Flow Underwriting Data Scientist role focused on building machine learning models for cash flow underwriting in the fintech domain. The role involves leveraging advanced ML techniques to engineer predictive signals from banking and transaction data to forecast creditworthiness and customer behavior. It requires building models through all phases of development, from design to implementation, using technologies like Python, Spark, and AWS. | Post-train | 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 |
| Senior Lead Software Engineer, Full Stack Lead the design and implementation of AI-native software engineering harnesses that use approved models, coding agents, and developer workflows in governed control-plane capabilities. Build agentic workflow orchestration systems that support planning, code generation, validation, retry loops, human checkpoints, and controlled promotion through delivery environments. Define and implement typed input/output contracts, schemas, metadata capture, and workflow state models that make agentic execution auditable and repeatable. Develop trace capture, observability, and debugging capabilities for AI-assisted engineering workflows, including prompt/output lineage, tool-call traces, model/runtime metadata, and failure analysis. Integrate evaluation results into CI/CD and release workflows, including quality gates for fidelity, build correctness, accessibility, security, performance, and human-review outcomes. | AgentServe | 7 |
| Lead Machine Learning Engineer (Manager IC) Lead Machine Learning Engineer role focused on building and deploying AI-powered solutions for Risk management within Capital One. The role involves designing, developing, testing, and deploying AI software components, including LLM inference, similarity search, guardrails, and agentic AI. It also includes fine-tuning models, managing production models, and optimizing data pipelines, with a strong emphasis on responsible and explainable AI. | AgentServe | 7 |
| Principal Associate, Data Science - Model Risk Office This role is in Capital One's Model Risk Office, focusing on defending the company against model failures and improving decision-making through models. The Principal Associate, Data Science will partner with cross-functional teams to identify and quantify model risks, lead teams of data scientists in building ML models to challenge existing production models, and contribute to the model governance framework. The role also involves leading model validation across various business domains and presenting identified risks to executives. The ideal candidate is innovative, creative, a leader, technical, and statistically-minded, with experience in model building, validation, and backtesting. | Ship | 7 |
| Senior Associate, Data Scientist Data Scientist role focused on building machine learning models for Capital One's Commercial Bank Team, involving forecasting, analytical tools, and model implementation. The role requires leveraging technologies like Python, AWS, and Spark to analyze large datasets and deliver data-driven solutions. | Post-train | 7 |
| Lead Software Engineer, Full Stack Lead Software Engineer focused on building AI-native software engineering harnesses and agentic workflow orchestration systems for governed AI model and coding agent delivery within an enterprise environment. The role involves defining contracts, developing trace capture and observability, integrating evaluation results into CI/CD, and building reusable APIs and tooling to enable AI-native engineering practices. | AgentEval Gate | 7 |
| Machine Learning Engineer (AI Foundations) Machine Learning Engineer focused on productionizing AI/ML applications and systems at scale within a fintech company. The role involves designing, building, and delivering ML models and components, informing ML infrastructure decisions, writing and testing application code, and collaborating in an Agile team. Responsibilities include retraining, maintaining, and monitoring models in production, leveraging cloud-based architectures, constructing data pipelines, and ensuring CI/CD best practices, code security, and responsible AI. Basic qualifications include a Bachelor's degree and experience in data-intensive solutions and programming. Preferred qualifications include cloud experience, open-source contributions, and building production-ready data pipelines. | ShipServe | 7 |
| Senior Lead Machine Learning Engineer (Intelligent Foundations and Experiences) Senior Lead Machine Learning Engineer focused on building and scaling AI/ML capabilities for Credit and Financial Risk Management products. The role involves designing, building, and delivering AI-powered products, including LLM inference and agentic AI, and optimizing ML infrastructure and data pipelines for production at scale. | AgentServe | 7 |
| Manager, Data Scientist, Travel Intelligence, Flights Manager, Data Scientist for Capital One Travel's Flights domain, focusing on building in-house machine learning solutions to create personalized and seamless customer experiences. The role involves end-to-end technical strategy, model development, and leveraging large datasets for fintech product innovation in the travel space. | ShipServe | 7 |
| Manager, Data Scientist - Innovation Hub Manager of Data Scientists focused on designing and deploying generative AI and AI-powered capabilities within a financial services company. The role involves building ML models through all development phases, leveraging various technologies, and translating complex work into business goals. It emphasizes innovation, creativity, leadership, and technical expertise in data science and machine learning. | ShipPost-train | 7 |
| Senior Manager, Data Science - Anti-Money Laundering Modeling and Advanced Data Insights Capital One is seeking a Senior Manager of Data Science to lead the Anti-Money Laundering (AML) Modeling and Advanced Data Insights team. This role involves modernizing AML identification processes using advanced analytics, statistics, and machine learning. The team develops predictive models, monitoring dashboards, and reporting, with end-to-end responsibility for production models. The role requires partnering with cross-functional teams, leveraging technologies like Python, AWS, and Spark, and building ML models through all development phases. | Post-train | 7 |
| Manager, Data Science - Consumer Identity Machine Learning Manager of Data Science focused on Consumer Identity Machine Learning within Capital One's AI foundation organization. The role involves building and operationalizing real-time, personalized ML models for customer experiences, fraud prevention, and data accuracy, serving over 50 million customers. It requires working across the data science lifecycle, from design and training to evaluation and productionization, in a regulated fintech environment. | Post-trainServe | 7 |
| Senior Manager, Data Science - Model Risk Office Senior Manager, Data Science for Capital One's Model Risk Office, focusing on evaluating risk for Card Fraud models. The role involves building challenger models using advanced algorithms and providing mentorship. This is a management role within a fintech domain, focused on the application and risk assessment of AI/ML models in a production environment. | ShipPost-train | 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 |
| Manager, Data Science - Model Risk Office Manager for a Model Validation team within the Model Risk Office, responsible for validating payment network business models (fraud, AML, counterparty risk, financial models) in accordance with regulatory guidance and internal policies. The role involves assessing statistical and machine learning models, communicating technical concepts to diverse stakeholders, and leveraging open-source technologies for continuous improvement. | Ship | 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 |
| Senior Associate, Data Scientist - US Card (Resiliency Intelligence) This role focuses on building and implementing supervised and reinforcement learning models to predict customer needs and recommend optimal financial solutions for customers facing hardship. The models directly impact millions of customers and the business's income, requiring development through all phases from design to implementation in production environments. | Post-train | 7 |
| Senior Director, Data Science - Head of Fair Lending Analytics - Fair & Responsible Banking Compliance Senior Director of Data Science to lead the Fair Lending Analytics group within Capital One's Compliance and Ethics Department. This role involves leading a team of data scientists and analysts to identify and mitigate fair lending and compliance risks, conduct data analyses of lending decisions, and provide guidance on business activities. The position requires strong leadership, technical expertise in data science and machine learning, and experience in developing and implementing fair lending and responsible data use processes, particularly in the context of AI in credit. | ShipPost-train | 7 |
| Manager, Data Scientist - US Card (Resiliency Intelligence) Capital One is seeking a Manager, Data Scientist for their US Card Resiliency Intelligence team. This role focuses on building machine learning models using supervised and reinforcement learning to predict customer needs and recommend personalized solutions for financial stability. The team uses Python, XGBoost, scikit-learn, and statsmodels, deploying models that impact millions of customers daily in both analytical and production environments. The ideal candidate is creative, technical, statistically-minded, and proficient with large datasets and open-source tools. | Post-train | 7 |
| Principal Associate, Data Scientist - Retail Bank Valuations Data Science This role focuses on building and implementing next-generation customer valuation models for the Retail Bank to improve marketing efficiency and drive account growth. It involves data and model pipelining, machine learning, and model operations using Python and ML libraries. | Post-train | 7 |
| Principal Data Scientist - AI Foundations, Specialist Models The Entity Resolution Systems team builds and ships state-of-the-art machine learning solutions for entity resolution within the enterprise, impacting marketing and customer servicing. The role involves partnering with cross-functional teams to build a next-generation entity resolution solution using cutting-edge ML, including transformers and graph ML, and leveraging agentic AI tools and workflows. The ideal candidate is innovative, creative, technical, statistically-minded, and a data guru. | AgentPost-train | 7 |
| Lead Software Engineer, Full Stack-IC Lead Software Engineer to architect and launch conversational AI experiences for millions of Capital One customers, leveraging Generative AI capabilities. The role involves leading a team, developing cloud-based solutions, and collaborating with product managers. | Agent | 7 |
| Senior Lead Software Engineer (Golang + EKS, Kubernetes, LLM's + Agentic flows + control/data planes) Senior Lead Software Engineer role focused on building platforms for AI/ML solutions at scale within Capital One's Machine Learning Experience Team (MLX Tech). The role involves leading development teams, collaborating on cloud-based solutions, and leveraging technologies like Golang, EKS, Kubernetes, LLMs, and agentic workflows to implement AI/ML for customer assistance in a financial context. | Agent | 7 |
| Lead Data Engineer - HR Tech Lead Data Engineer role focused on HR Tech, involving the design, development, and implementation of technical solutions. The role specifically mentions building and deploying GenAI and Agentic AI solutions, indicating a focus on developing AI-powered systems. | Agent | 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 |
| Director, Software Engineering Director of Software Engineering at Capital One, leading teams to build AI-based marketing applications, focusing on an automated agentic flow for campaign creation and management. The role involves mentoring engineers, collaborating with product managers, and improving software engineering practices within an Agile and AWS environment. Key technologies include Python, Java, Vue.js, Postgres, Dynamo DB, and vector stores like Milvus. | Agent | 7 |
| Principal Associate, Data Scientist - Retail Bank Data Scientist role focused on building machine learning models for retail banking operations, including time series forecasting and optimization, with an emphasis on interpretable outputs. The role involves the full ML lifecycle from design to implementation and works with large volumes of data. | Post-train | 7 |
| Staff Software Engineer - Machine Learning Staff Software Engineer - Machine Learning role at Capital One in London. This role focuses on owning and driving the ML/AI technical strategy for UK use cases, leading ML engineering efforts across multiple teams, and providing technical consultancy. It involves defining best practices, driving MLOps standards, and collaborating with data science and enterprise platform teams. The role requires deep expertise in Python, ML engineering, MLOps, cloud-native architectures, ML frameworks, and Gen AI/Agentic frameworks like LangGraph, LangChain, VectorDBs, and RAG. Experience in designing and scaling low-latency, customer-facing ML/AI architectures and understanding responsible AI practices are also key. | AgentServe | 7 |
| Senior Manager, Data Scientist - Travel Intelligence Senior Manager, Data Scientist for Capital One's Travel Intelligence team, focusing on the Hotels domain. The role involves owning the end-to-end technical strategy for Search & Sort and Pricing Optimization, moving from offline models to real-time ML solutions. Responsibilities include partnering with cross-functional teams, leveraging technologies like Python and AWS, and building ML models through all development phases. | Post-train | 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 Data Scientist (Model Developer) / Deep Learning Practitioner Lead Data Scientist role focused on developing and deploying deep learning models for underwriting capabilities in a fintech company. The role involves advancing existing models, building neural networks, transforming multi-modal inputs, and driving projects from prototype to production. Requires experience in sequential data, model development leadership, and modern ML frameworks. | Post-trainServe | 7 |
| Data Scientist / Statistician (Model Developer) Develop and maintain machine learning models for underwriting in the financial services market, focusing on feature engineering, insight generation, and handling various data types including sequential and multi-modal data. Requires strong statistics, ML, Python, and Model Risk Management experience. | Post-train | 7 |
| Senior Manager, Data Science - Credit Review This role focuses on building and challenging existing statistical and machine learning models within the credit review domain at Capital One. The Senior Manager will lead a team, leverage technologies like Python, AWS, and Spark, and partner with cross-functional teams to deliver data-driven solutions that impact risk management and enterprise outcomes. The role requires strong technical and statistical skills, with experience in model building, validation, and various ML techniques. | Post-train | 7 |
| Senior Manager, Data Science - Model Risk Office Senior Manager for Model Risk Office at Capital One, focusing on identifying and quantifying risks associated with models, including Generative AI. The role involves building ML models to challenge existing production models and contributing to the governance framework for future models. It requires validating models across various business domains and communicating risks to executives. The ideal candidate is innovative, creative, a leader, technically proficient, statistically minded, and skilled in handling large datasets. | ShipPost-train | 7 |
| Senior Lead Machine Learning Engineer Senior Lead Machine Learning Engineer at Capital One, focused on productionizing ML applications and systems at scale within a fintech domain. Responsibilities include designing, building, and delivering ML models, optimizing ML infrastructure, writing and testing code, automating deployment, and maintaining models in production. The role involves leveraging cloud platforms, constructing data pipelines, and ensuring responsible AI practices. | ShipServe | 7 |