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 - LLM Customization Team Capital One's LLM Customization team is seeking a Principal Associate Data Scientist to work on GenAI models. The role involves creating high-quality data for training and testing, building capabilities for evaluating and monitoring generative models, and developing horizontal capabilities like search, summarization, RAG, and agentic workflows for production applications. The candidate will partner with cross-functional teams, leverage technologies like Pytorch, AWS, Hugging Face, LangChain, and VectorDBs, and adapt/finetune LLMs for customer-facing applications. Responsibilities include building ML and NLP models through all development phases, from design to evaluation and validation, and operationalizing them in production systems serving over 80 million customers. Experience in training language models, explainability, RLHF, and delivering models at scale is required. | DataPost-train |
| 8 |
| Principal Associate, Data Scientist - Deposit Forecasting and Pricing This role focuses on developing and supporting econometric time-series models for balance sheet portfolios within Capital One's Retail and Direct Bank. The Data Scientist will apply quantitative methods to improve business performance, demonstrate a track record in behavior model development, and communicate complex models to various stakeholders including management, model risk office, and regulators. The ideal candidate has hands-on experience with data science solutions using open-source tools and cloud platforms, with specific experience in time series, clustering, classification, sentiment analysis, and deep learning. | Data | 7 |
| Principal Associate, Data Scientist - Frontier AI in Customer Protection This role focuses on building knowledge graphs and graph algorithms for fraud prevention within the financial sector. The Data Scientist will leverage technologies like Python, AWS, Spark, Gremlin, and NeptuneDB to uncover hidden connections in data, pilot graph modeling algorithms through development phases, and partner with cross-functional teams to deliver fraud defenses. The ideal candidate is customer-focused, innovative, technically proficient, and skilled in handling large datasets. | Data | 7 |
| Senior Associate, Data Scientist - Cash Flow Underwriting This role focuses on leveraging advanced machine learning techniques to identify and engineer predictive signals from cash flow and transaction data for risk and usage models in the fintech domain. The data scientist will transform raw banking data into high-value features to forecast creditworthiness and customer behavior, building ML models through all phases of development. | Data | 7 |