Applied Researcher I, AI Foundations, LLM Core, Agentic AI

Capital One

Applied Researcher building foundation models and agentic AI for Capital One, an information-based technology company transforming banking. Conducting applied research and scaling AI products.

Posted 8/17/2026full-timeNew York City • California, Massachusetts, New York, Virginia • 🇺🇸 United StatesJuniorMid-Level💰 $218,700 - $272,300 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing AI foundation models and solutions, leveraging advanced methodologies and tools such as PyTorch and AWS. Capable of translating complex research into actionable business goals while delivering scalable models and code solutions.

Highest-signal resume keywords

AI Foundation Model DevelopmentDeep Learning Model OptimizationApplied Research ExperienceCloud Computing PlatformsPublications in Machine Learning

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI Methodology FoundationsLarge Deep Learning ModelsTraining OptimizationSelf-Supervised LearningRobustnessExplainabilityReinforcement Learning from Human FeedbackModel AdaptationFine-Tuning LLMsTransfer Learning

Soft Skills

Problem SolvingStakeholder EngagementTalent DevelopmentAutonomous Project Management

Tools & Technologies

PyTorchAWS UltraclustersHugging FaceLightningVectorDBs

Certifications & Qualifications

PhD in Relevant FieldM.S. in Relevant Field

Industry Keywords

Applied ResearchNatural Language ProcessingMachine LearningAI-Powered ProductsEmerging Technologies

Tech Stack

Tools & technologies

AWSCloudPyTorch

About the role

Key responsibilities & impact

Partner with data scientists, software engineers, machine learning engineers, and product managers to deliver AI-powered products
Leverage PyTorch, AWS Ultraclusters, Hugging Face, Lightning, VectorDBs, and other technologies to analyze numeric and textual data
Build AI foundation models through design, training, evaluation, validation, and implementation
Conduct applied research to advance AI developments into customer experiences
Translate complex research into tangible business goals
Research and evaluate emerging technologies and apply state-of-the-art methods
Define and solve large, ambiguous problems
Challenge conventional thinking and improve the status quo with stakeholders
Develop talent within and beyond the team
Deliver models at scale across training data and inference volumes
Deliver libraries, platform-level code, or solution-level code to existing products
Own and pursue a research agenda, selecting impactful problems and carrying out long-running projects autonomously

Requirements

What you’ll need

Currently has or is in the process of obtaining a PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields, with the degree obtained by the scheduled start date; or an M.S. in those fields plus 2 years of Applied Research experience
Hands-on experience developing AI foundation models and solutions using open-source tools and cloud computing platforms
Deep understanding of AI methodology foundations
Experience building large deep learning models for language, images, events, or graphs
Expertise in one or more of training optimization, self-supervised learning, robustness, explainability, or RLHF
Track record of delivering models at scale in training data and inference volumes
Experience delivering libraries, platform-level code, or solution-level code to existing products
Machine learning accomplishments such as first-author publications or projects
Ability to own and pursue a research agenda and autonomously conduct long-running projects
Preferred: PhD in a relevant field or LLM-focused PhD in NLP, or a master's degree with 5 years of industrial NLP research experience
Preferred: publications related to pre-training large language models, deep learning theory, or major ML/AI conferences
Preferred: experience training large language models from scratch, optimizing 10B+ models, compiler design, fine-tuning LLMs, transfer learning, model adaptation, model guidance, and deploying fine-tuned LLMs
Capital One will consider sponsoring a new qualified applicant for employment authorization

Benefits

Comp & perks

Performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI)
Comprehensive, competitive, and inclusive health, financial, and other benefits supporting total well-being
Reasonable accommodations for applicants who require them