Applied Researcher 5 – AI Foundations, Evaluation, Agentic Workflows

Capital One

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

Posted 11/6/2026full-timeUnited StatesJuniorMid-Level💰 $262,500 - $326,800 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and deploying AI foundation models, leveraging advanced methodologies in deep learning and machine learning. Proven ability to translate complex research into actionable business outcomes while leading cross-functional teams and mentoring junior researchers.

Highest-signal resume keywords

PhD In Computer ScienceDeep Learning Model DevelopmentExperience With PyTorchApplied Research ExperienceExpertise In Training Optimization

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI MethodologiesLarge Deep Learning ModelsSelf-Supervised LearningRobustnessExplainabilityReinforcement Learning From Human Feedback (RLHF)Graph Neural NetworksSequential ModelsMultivariate Time SeriesProduction Real-Time Environments

Soft Skills

LeadershipCollaborationResearch Agenda OwnershipMentoring

Tools & Technologies

AWS UltraclustersHugging FaceLightningOpen-Source ToolsCloud Computing PlatformsPyTorch GeometricDGL

Industry Keywords

Applied ResearchMachine LearningData AnalysisCustomer ExperienceEmerging 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, 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
Lead cross-functional research threads connecting prototype model development and deployment with ML engineering partners
Ensure scientific insights translate into production impact
Research and evaluate emerging technologies and state-of-the-art methods
Identify and improve the status quo with stakeholders
Develop talent across the team and broader organization

Requirements

What you’ll need

Currently has, or is in the process of obtaining, PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields, with the required degree obtained on or before the scheduled start date, plus 2 years of experience in Applied Research
Alternatively, M.S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 4 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 or improvements, demonstrated through accomplishments such as first-author publications or projects
Ability to own and pursue a research agenda, choose 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
PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering, or related fields (preferred)
PhD focus on geometric deep learning, including Graph Neural Networks, Sequential Models, or Multivariate Time Series (preferred)
Multiple papers on graph and sequential data training topics at KDD, ICML, NeurIPS, or ICLR (preferred)
Experience scaling graph models to more than 50 million nodes (preferred)
Experience with large-scale deep-learning-based recommender systems (preferred)
Experience with production real-time and streaming environments (preferred)
Contributions to open-source frameworks such as PyTorch Geometric or DGL (preferred)
Proposed new methods for inference or representation learning on graphs or sequences (preferred)
Experience with datasets containing more than 100 million users (preferred)
Ability to design and lead small-scale research initiatives and guide junior researchers or engineers through experimental design and evaluation (preferred)
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 with disabilities
Equal opportunity and non-discrimination commitment
Drug-free workplace