Applied Researcher 5 – AI Foundations

Capital One

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

Posted 11/3/2026full-timeUnited StatesJuniorMid-Level💰 $262,500 - $326,800 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and optimizing AI foundation models, with a strong focus on large language models and deep learning methodologies. Proven ability to translate complex research into practical applications and deliver impactful AI solutions in collaboration with cross-functional teams.

Highest-signal resume keywords

PhD In Computer ScienceExperience In Applied ResearchPublications In Deep Learning TheoryExperience With Large Language ModelsExpertise In Training Optimization

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Deep LearningMachine LearningNatural Language ProcessingModel OptimizationModel CompressionTransfer LearningSelf-Supervised LearningGradient CheckpointingQuantizationTraining Parallelism

Soft Skills

Autonomous Research AgendaCollaborationCommunication

Tools & Technologies

PyTorchAWS UltraclustersHugging FaceLightningOpen-Source ToolsCloud Computing Platforms

Industry Keywords

Applied MathematicsComputer EngineeringElectrical EngineeringAI Methodology FoundationsPublications At ACLPublications At NeurIPSPublications At ICMLPublications At ICLRModel AdaptationRobustness

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 numeric and textual data
Build AI foundation models through design, training, evaluation, validation, and implementation
Conduct applied research and advance AI developments into 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

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
PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering or related fields preferred
LLM PhD focus on NLP or Masters with 5 years of industrial NLP research experience
Multiple publications on topics related to pre-training of large language models
Experience as a member of a team that trained a large language model from scratch with 10B+ parameters and 500B+ tokens
Publications in deep learning theory
Publications at ACL, NAACL, EMNLP, NeurIPS, ICML, or ICLR
Optimization PhD focused on optimizing training of very large deep learning models
Multiple years of experience and/or publications in model sparsification, quantization, training parallelism/partitioning design, gradient checkpointing, or model compression
Experience optimizing training for a 10B+ model
Deep knowledge of deep learning algorithmic and/or optimizer design
Experience with compiler design
Finetuning PhD focused on guiding LLMs with further tasks
Demonstrated knowledge of transfer learning, model adaptation, and model guidance
Experience deploying a fine-tuned large language model
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
Track record of high-quality machine learning ideas, such as first-author publications or projects
Ability to own and pursue a research agenda autonomously

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
Employment authorization sponsorship consideration for a new qualified applicant