Applied Researcher 4 – AI Foundations, LLM Customization, Finetuning, Reinforcement Learning

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 deploying AI foundation models, with a strong focus on deep learning methodologies and large-scale data analysis. Proven ability to translate complex AI research into practical business applications and collaborate effectively with cross-functional teams.

Highest-signal resume keywords

PhD In Computer ScienceDeep Learning Model DevelopmentExperience With PyTorchApplied Research ExperienceLarge Language Model Training

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI MethodologiesTraining OptimizationSelf-Supervised LearningRobustnessExplainabilityReinforcement Learning From Human FeedbackData AnalysisModel EvaluationModel ValidationModel Implementation

Soft Skills

CollaborationResearch Agenda OwnershipProblem SelectionAutonomous Project Management

Tools & Technologies

AWS UltraclustersHugging FaceLightningOpen-Source ToolsCloud Computing Platforms

Industry Keywords

Applied ResearchNatural Language ProcessingGeometric Deep LearningLarge-Scale Deep LearningData PreparationDataset CurationTokenization

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 and advance AI developments into customer experiences
Translate complex research into tangible business goals
Collaborate with senior researchers on internal technical reports or conference submissions summarizing novel methods or findings

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; degree must be obtained by the scheduled start date, or an M.S. in 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, 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 Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering, or related fields
Preferred LLM qualifications: PhD focus on NLP or Master's with 5 years of industrial NLP research experience; publications on large language model pre-training; experience training a large language model from scratch; publications in deep learning theory or specified conferences
Preferred behavioral-model qualifications: PhD focus in geometric deep learning; papers on graph and sequential data; scaling graph models beyond 50m nodes; large-scale deep-learning recommender systems; production real-time and streaming environments; contributions to PyTorch Geometric or DGL; inference or representation-learning methods; datasets with 100m+ users
Preferred finetuning qualifications: PhD focused on guiding LLMs with further tasks; knowledge of transfer learning, model adaptation, and model guidance; experience deploying a fine-tuned large language model
Preferred data-preparation qualifications: publications or contributions involving tokenization, data quality, dataset curation, labeling, or open-source corpora/libraries
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
Employment authorization sponsorship consideration for a new qualified applicant
Reasonable accommodations for applicants who require them