Applied Researcher, Senior

Capital One

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

Posted 11/4/2026full-timeUnited 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, with a strong focus on deep learning methodologies and large-scale model training. Proven ability to translate complex research into actionable business outcomes while collaborating effectively with cross-functional teams.

Highest-signal resume keywords

PhD In Electrical Engineering, Computer Engineering, Computer Science, AI, MathematicsExperience Building Large Deep Learning ModelsExpertise In Training Optimization, Self-Supervised Learning, Robustness, Explainability, RLHFTrack Record Of Delivering Models At ScaleHands-On Experience Developing AI Foundation Models Using Open-Source Tools

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Deep LearningMachine LearningAI MethodologiesModel TrainingModel EvaluationModel ValidationNLPModel CompressionTransfer LearningCompiler Design

Soft Skills

CollaborationResearch Agenda OwnershipProblem-SolvingCommunication

Tools & Technologies

PyTorchAWS UltraclustersHugging FaceLightningOpen-Source ToolsCloud Computing Platforms

Industry Keywords

Applied ResearchLarge Language ModelsDeep Learning TheoryPublications In AI ConferencesTraining Data Optimization

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 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
Collaborate with senior researchers on internal technical reports or conference submissions
Research and evaluate emerging technologies and state-of-the-art methods
Identify and pursue impactful machine learning research problems and long-running 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; required degree must be obtained on or before the scheduled start date
Alternatively, an M.S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 2 years of experience in Applied Research
Deep understanding of the foundations of AI methodologies
Experience building large deep learning models involving 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 by 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
Preferred: PhD in a related technical field
Preferred: LLM PhD focus on NLP or Master's with 5 years of industrial NLP research experience
Preferred: Multiple publications related to pre-training large language models
Preferred: Experience training 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: PhD focused on optimizing training of very large deep learning models
Preferred: Experience and/or publications in model sparsification, quantization, training parallelism/partitioning design, 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: PhD focused on fine-tuning LLMs
Preferred: Knowledge of transfer learning, model adaptation, and model guidance
Preferred: Experience deploying a fine-tuned large language model

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