Applied Researcher 5 – AI Foundations, LLM, Optimization, Finetuning

Capital One

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

Posted 9/23/2026full-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, particularly in large language models, with a strong focus on applied research and machine learning methodologies. Proven ability to translate complex research into impactful business solutions while collaborating with cross-functional teams.

Highest-signal resume keywords

PhD In Computer ScienceExperience In Applied ResearchLarge Language Model TrainingDeep Learning OptimizationExperience With PyTorch

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Applicant Tracking System Keywords

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Hard Skills

Applied ResearchLarge Language Model TrainingDeep Learning OptimizationModel SparsificationQuantizationTraining ParallelismGradient CheckpointingModel CompressionTransfer LearningSelf-Supervised Learning

Soft Skills

Independent Research AgendaCollaboration

Tools & Technologies

PyTorchAWS UltraclustersHugging FaceLightningOpen-Source ToolsCloud Computing Platforms

Industry Keywords

AI MethodologiesNLP ResearchDeep Learning TheoryMachine Learning EngineeringPublications In ACLNAACLEMNLPNeurIPSICMLICLR

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 banking products
Use PyTorch, AWS Ultraclusters, Hugging Face, Lightning, and related technologies to analyze large numeric and textual datasets
Build AI foundation models through design, training, evaluation, validation, and implementation
Conduct applied research and advance emerging AI capabilities 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; or M.S. in those 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 Master's with 5 years of industrial NLP research experience
Multiple publications on pre-training of large language models
Member of a team that has trained 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
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
PhD focused on guiding LLMs with supervised finetuning, instruction-tuning, dialogue-finetuning, or parameter tuning
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 methodologies
Experience building large deep learning models and expertise in 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 and independently carry out long-running projects
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