Applied Researcher 4, AI Foundations, LLM Customization and Finetuning

Capital One

. Partner with data scientists, software engineers, machine learning engineers, and product managers to deliver AI-powered products .

Posted 10/5/2026full-timeUnited StatesJuniorMid-Level💰 $218,700 - $272,300 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and deploying AI foundation models, with a strong focus on deep learning methodologies and large-scale model training. Proven ability to translate complex research into actionable business strategies while collaborating effectively with cross-functional teams.

Highest-signal resume keywords

PhD In Electrical EngineeringDeep Learning Model DevelopmentAI Methodologies ExpertiseExperience With PyTorchCloud Computing Platforms

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Applicant Tracking System Keywords

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Hard Skills

Applied ResearchTraining OptimizationSelf-Supervised LearningRobustnessExplainabilityReinforcement Learning From Human FeedbackModel AdaptationData PreparationTokenizationDataset Curation

Soft Skills

CollaborationAutonomous Project ManagementProblem SolvingTalent Development

Tools & Technologies

AWS UltraclustersHugging FaceLightningOpen-Source Tools

Industry Keywords

Natural Language ProcessingMachine LearningAI ResearchDeep Learning TheoryPublications In AI

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
Use PyTorch, AWS Ultraclusters, Hugging Face, Lightning, and other technologies to analyze large volumes of numeric and textual data
Build AI foundation models through design, training, evaluation, validation, and implementation
Conduct applied research to advance AI developments into next-generation customer experiences
Translate complex research into tangible business goals
Collaborate with senior researchers to prepare internal technical reports or conference submissions
Research and evaluate emerging technologies and state-of-the-art methods
Identify and improve solutions to undefined problems and conventional approaches
Support talent development across the team

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 one of these fields plus 2 years of experience in Applied Research
Deep understanding of the foundations of AI methodologies
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
Track record of high-quality machine-learning ideas, demonstrated through accomplishments such as first-author publications or projects
Ability to own and pursue a research agenda, select impactful research problems, and autonomously carry out long-running projects
Hands-on experience developing AI foundation models and solutions using open-source tools and cloud computing platforms
Preferred: PhD in a related technical field
Preferred: LLM PhD focus on NLP or Master's with 5 years of industrial NLP research experience
Preferred: Publications related to pre-training large language models
Preferred: Team experience training a large language model from scratch with 10B+ parameters and 500B+ tokens
Preferred: Publications in deep learning theory and at ACL, NAACL, EMNLP, NeurIPS, ICML, or ICLR
Preferred: Finetuning PhD focused on guiding LLMs with further tasks
Preferred: Knowledge of transfer learning, model adaptation, and model guidance
Preferred: Experience deploying a fine-tuned large language model
Preferred: Data-preparation publications on tokenization, data quality, dataset curation, or labeling
Preferred: Contribution to a major open-source corpus or relevant open-source libraries
Preferred: Ability to reproduce and extend peer-reviewed AI research using modern open-source frameworks
Preferred: Experience designing controlled experiments and documenting reproducibility results
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