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 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 |
|---|---|---|
| Applied Researcher I (AI Foundations, LLM Customization, Finetuning, Reinforcement Learning) Applied Researcher role focused on AI Foundations, LLM Customization, Finetuning, and Reinforcement Learning within a fintech company. The role involves partnering with cross-functional teams to deliver AI-powered products, leveraging technologies like Pytorch and VectorDBs, and building AI foundation models through all development phases. It emphasizes applied research to improve customer experiences and requires a deep understanding of AI methodologies, experience building large deep learning models, and a track record of delivering models at scale. | Post-trainPretrain | 9 |
| Applied Researcher II Applied Researcher II role at Capital One focused on building AI foundation models from design through training, evaluation, validation, and implementation. The role involves applied research to create next-generation customer experiences and delivering models at scale. Requires a strong technical background in deep learning, model optimization, and experience with open-source tools and cloud platforms. |
| Post-trainPretrain |
| 9 |
| Applied Researcher I (AI Foundations, LLM Customization, Finetuning, Reinforcement Learning) Applied Researcher I role focused on AI Foundations, LLM Customization, Finetuning, and Reinforcement Learning within Capital One's fintech domain. The role involves partnering with cross-functional teams to deliver AI-powered products, building AI foundation models through all development phases, and conducting applied research to enhance customer experiences. Requires a strong technical background in deep learning, model training, and experience with open-source tools and cloud platforms. | Post-trainPretrain | 9 |
| Applied Researcher II Applied Researcher II role focused on building AI foundation models through all phases of development, from design through training, evaluation, validation, and implementation. The role involves high-impact applied research to push the latest AI developments into customer experiences, leveraging technologies like Pytorch, AWS, Huggingface, and VectorDBs. Requires a PhD or MS with significant experience in AI/ML, with expertise in areas like training optimization, self-supervised learning, robustness, explainability, or RLHF, and a track record of delivering models at scale. | Post-trainPretrain | 9 |
| Applied Researcher I (AI Foundations, LLM Core and Agentic AI) Applied Researcher I focused on AI Foundations, LLM Core, and Agentic AI at Capital One. The role involves partnering with cross-functional teams to deliver AI-powered products, leveraging technologies like PyTorch, AWS, Huggingface, and VectorDBs. Responsibilities include building AI foundation models through all development phases (design, training, evaluation, validation, implementation) and conducting applied research to create next-generation customer experiences. Requires a PhD or MS with experience in AI/ML, with a strong understanding of AI methodologies, experience building large deep learning models (language, images, events, graphs), and expertise in optimization, self-supervised learning, robustness, explainability, or RLHF. An engineering mindset with a track record of delivering models at scale and experience in delivering libraries or platform code is essential. A track record of high-quality ideas or improvements demonstrated by publications or projects is also required. | Post-trainAgent | 9 |
| Applied Researcher I (AI Foundations, LLM Customization, Finetuning, Reinforcement Learning) Applied Researcher role focused on AI Foundations, LLM Customization, Finetuning, and Reinforcement Learning within a fintech company. The role involves partnering with cross-functional teams to deliver AI-powered products, leveraging technologies like Pytorch and VectorDBs, and building AI foundation models through all development phases. It emphasizes applied research to improve customer experiences and requires a deep understanding of AI methodologies, experience building large deep learning models, and a track record of delivering models at scale. | Post-trainPretrain | 9 |
| Applied Researcher I (AI Foundations, LLM Customization, Finetuning, Reinforcement Learning) Applied Researcher role focused on AI Foundations, LLM Customization, Finetuning, and Reinforcement Learning within a fintech company. The role involves partnering with cross-functional teams to deliver AI-powered products, leveraging technologies like Pytorch and VectorDBs, and building AI foundation models through all development phases. It emphasizes applied research to improve customer experiences and requires a deep understanding of AI methodologies, experience building large deep learning models, and a track record of delivering models at scale. | Post-trainPretrain | 9 |
| Applied Researcher I Applied Researcher I role focused on building AI foundation models, engaging in applied research to improve customer experiences, and delivering AI-powered products. The role involves training optimization, self-supervised learning, robustness, explainability, and RLHF, with an emphasis on delivering models at scale. | Post-trainPretrain | 9 |
| Applied Researcher II This role is for an Applied Researcher II at Capital One focused on building AI foundation models and applying state-of-the-art AI to customer-facing products. The role involves research, development, training, evaluation, and implementation of AI models, with a strong emphasis on pushing AI capabilities into next-generation customer experiences. The candidate will work with cross-functional teams and leverage various technologies including Pytorch, AWS, Huggingface, and VectorDBs. Experience in training optimization, self-supervised learning, robustness, explainability, RLHF, and delivering models at scale is required. A PhD or MS in a related field with significant research experience is preferred, along with a publication record. | Post-trainPretrain | 9 |
| Applied Researcher II (AI Foundations, LLM Core and Agentic AI) Applied Researcher II at Capital One focused on AI Foundations, LLM Core, and Agentic AI. The role involves partnering with cross-functional teams to deliver AI-powered products, leveraging technologies like Pytorch and VectorDBs. Responsibilities include building AI foundation models through all development phases (design, training, evaluation, validation, implementation) and engaging in applied research to advance customer experiences. The ideal candidate has a deep understanding of AI methodologies, experience building large deep learning models (language, images, events, graphs), expertise in optimization, self-supervised learning, robustness, explainability, or RLHF, and a track record of delivering models at scale. Experience with LLMs, including pre-training and fine-tuning, is highly preferred. | Post-trainAgent | 9 |
| Applied Researcher I (AI Foundations) Applied Researcher I (AI Foundations) at Capital One, focusing on building AI foundation models through all phases of development, from design through training, evaluation, validation, and implementation. The role involves applied research to push state-of-the-art AI into customer experiences, leveraging technologies like Pytorch, AWS, Huggingface, and VectorDBs. Requires a PhD or MS with experience in AI/ML, deep learning, and delivering models at scale. | Post-trainPretrain | 9 |
| Sr. Distinguished Applied Researcher Sr. Distinguished Applied Researcher at Capital One focused on building AI foundation models through all phases of development, from design through training, evaluation, validation, and implementation. The role involves high-impact applied research to integrate the latest AI developments into customer experiences, leveraging technologies like Pytorch, AWS, Huggingface, and VectorDBs. This individual contributor role requires guiding and mentoring teams, representing Capital One in the research community, and delivering AI-powered products and platforms. | Post-trainPretrain | 9 |
| Applied Researcher I Applied Researcher I role focused on building AI foundation models through all phases of development, from design through training, evaluation, validation, and implementation. The role involves high-impact applied research to push the latest AI developments into next-generation customer experiences, leveraging technologies like Pytorch, AWS Ultraclusters, Huggingface, Lightning, and VectorDBs. Experience with training optimization, self-supervised learning, robustness, explainability, and RLHF is desired, with a track record of delivering models at scale. | Post-trainPretrain | 8 |
| Applied Researcher II (AI Foundations, LLM Core and Agentic AI) Applied Researcher II focused on AI Foundations, LLM Core, and Agentic AI at Capital One. The role involves partnering with cross-functional teams to deliver AI-powered products, leveraging technologies like Pytorch, AWS, Huggingface, and VectorDBs. Responsibilities include building AI foundation models through all development phases (design, training, evaluation, validation, implementation) and conducting high-impact applied research to improve customer experiences. The ideal candidate has a strong technical background, experience building large deep learning models, expertise in areas like training optimization, self-supervised learning, robustness, explainability, or RLHF, and a track record of delivering models at scale. Experience with LLM pre-training, optimization, or fine-tuning is highly preferred. | Post-trainAgent | 8 |
| Applied Researcher II Applied Researcher II at Capital One focused on building AI foundation models through all phases of development, from design through training, evaluation, validation, and implementation. The role involves high-impact applied research to push AI developments into customer experiences, leveraging technologies like Pytorch, AWS, Huggingface, and VectorDBs. Requires experience building large deep learning models and delivering models at scale. | Post-trainServe | 8 |
| Applied Researcher I Applied Researcher I at Capital One focused on building AI foundation models and delivering them at scale for customer-facing products. The role involves partnering with cross-functional teams, leveraging various technologies, and conducting applied research to push AI capabilities into next-generation customer experiences. Requires a strong technical background in deep learning, model training, optimization, and a track record of delivering models in production. | Post-trainPretrain | 8 |
| Applied Researcher I Applied Researcher I role focused on building AI foundation models through all phases of development, from design through training, evaluation, validation, and implementation. The role involves high-impact applied research to push the latest AI developments into customer experiences, leveraging technologies like Pytorch, AWS, Huggingface, and VectorDBs. Requires a PhD or MS with experience in AI/ML, with expertise in areas like training optimization, self-supervised learning, robustness, explainability, or RLHF, and a track record of delivering models at scale. | Post-trainPretrain | 8 |
| Applied Researcher I Applied Researcher I role focused on building AI foundation models, engaging in applied research to push AI developments into customer experiences, and delivering models at scale. Requires experience in training optimization, self-supervised learning, robustness, explainability, or RLHF, with a track record of delivering libraries or platform code. | Post-trainPretrain | 8 |
| Applied Researcher II Applied Researcher II at Capital One focused on building AI foundation models and delivering them at scale for customer-facing products. The role involves partnering with cross-functional teams, leveraging technologies like Pytorch and VectorDBs, and engaging in applied research to push AI capabilities. | Post-trainPretrain | 8 |
| Applied Researcher I Applied Researcher I role focused on building AI foundation models through all phases of development, from design through training, evaluation, validation, and implementation. The role involves high-impact applied research to push the latest AI developments into customer experiences, leveraging technologies like Pytorch, AWS, Huggingface, and VectorDBs. Requires a PhD or MS with experience in AI/ML, with expertise in areas like training optimization, self-supervised learning, robustness, explainability, or RLHF, and a track record of delivering models at scale. | Post-trainPretrain | 8 |