Applied Researcher 5 – AI Foundations, LLM Core, Agentic AI

Capital One

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

Posted 9/18/2026full-timeSan Jose • California • United 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, with a strong focus on deep learning methodologies and large-scale model training. Proven ability to translate complex research into actionable business outcomes while leading research initiatives and mentoring junior team members.

Highest-signal resume keywords

PhD In Computer ScienceExperience With Large Language ModelsExpertise In Training OptimizationPublications In Deep Learning TheoryHands-On Experience With AI Foundation Models

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Deep LearningMachine LearningNatural Language ProcessingModel CompressionTraining OptimizationSelf-Supervised LearningRobustnessExplainabilityModel SparsificationGradient Checkpointing

Soft Skills

Research Agenda OwnershipMentoring Junior ResearchersExperimental Design Guidance

Tools & Technologies

PyTorchAWS UltraclustersHugging FaceLightningOpen-Source ToolsCloud Computing Platforms

Industry Keywords

Applied ResearchAI MethodologiesModel AdaptationTokenizationDataset Curation

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 related technologies to analyze large volumes of numeric and textual data
Build AI foundation models through design, training, evaluation, validation, and implementation
Conduct applied research and advance AI developments into next-generation customer experiences
Translate complex research into tangible business goals
Lead research threads bridging 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
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, 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 ideas or improvements in machine learning, 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
PhD focus on NLP or master's degree with 5 years of industrial NLP research experience
Multiple publications related to pre-training large language models
Experience training a large language model from scratch with 10B+ parameters and 500B+ tokens
Publications in deep learning theory and at ACL, NAACL, EMNLP, NeurIPS, ICML, or ICLR
Experience with model sparsification, quantization, training parallelism/partitioning, gradient checkpointing, or model compression
Experience optimizing training for a 10B+ model
Deep knowledge of deep learning algorithms and/or optimizer design
Experience with compiler design
Experience with supervised finetuning, instruction-tuning, dialogue-finetuning, or parameter tuning
Knowledge of transfer learning, model adaptation, and model guidance
Experience deploying a fine-tuned large language model
Publications or contributions related to tokenization, data quality, dataset curation, labeling, or major open-source corpora/libraries
Ability to design and lead small-scale research initiatives and guide junior researchers or engineers through experimental design and evaluation

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 may be considered for a new qualified applicant
Reasonable accommodations for applicants who require them