Applied Researcher 4 – 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💰 $218,700 - $272,300 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and optimizing AI foundation models using advanced methodologies and tools, with a strong focus on deep learning and natural language processing. Proven ability to translate complex research into practical applications and deliver impactful AI solutions at scale.

Highest-signal resume keywords

PhD In Electrical EngineeringDeep Learning Model DevelopmentAI Methodologies ExpertiseExperience With PyTorchNLP Research Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Deep LearningMachine LearningTraining OptimizationSelf-Supervised LearningRobustnessExplainabilityModel CompressionTransfer LearningData QualityDataset Curation

Soft Skills

CollaborationResearch Agenda OwnershipIndependent Execution

Tools & Technologies

PyTorchAWS UltraclustersHugging FaceLightningOpen-Source ToolsCloud Computing Platforms

Industry Keywords

Applied ResearchNatural Language ProcessingAI Foundation ModelsLarge Language ModelsControlled Experiments

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 and advance emerging AI developments into customer experiences
Translate complex research into tangible business goals
Collaborate with senior researchers on internal technical reports or conference submissions describing novel methods or findings
Research and evaluate emerging technologies and state-of-the-art methods
Identify impactful research problems and independently execute long-running research projects

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 on or before 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 or improvements, demonstrated through accomplishments such as first-author publications or projects
Ability to own and pursue a research agenda 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: PhD focus on NLP or a Master's degree with 5 years of industrial NLP research experience
Preferred: Multiple publications related to pre-training large language models
Preferred: Membership on a team that trained 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: Experience with model sparsification, quantization, training parallelism/partitioning, gradient checkpointing, or model compression
Preferred: Experience optimizing training for a 10B+ model
Preferred: Deep knowledge of deep-learning algorithmic and/or optimizer design
Preferred: Experience with compiler design
Preferred: Knowledge of supervised finetuning, instruction-tuning, dialogue-finetuning, or parameter tuning
Preferred: Knowledge of transfer learning, model adaptation, and model guidance
Preferred: Experience deploying a fine-tuned large language model
Preferred: Publications or contributions related to tokenization, data quality, dataset curation, labeling, or major open-source corpora
Ability to reproduce and extend peer-reviewed AI research using modern open-source frameworks
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
Drug-free workplace
Equal opportunity and non-discrimination protections