Applied Researcher, Level 4

Capital One

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

Posted 9/30/2026full-timeUnited StatesJuniorMid-Level💰 $218,700 - $272,300 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in AI methodologies and deep learning, with hands-on experience in building and optimizing large AI foundation models. Proven ability to translate complex research into actionable business outcomes and deliver high-quality machine learning solutions at scale.

Highest-signal resume keywords

PhD In Electrical EngineeringDeep Learning Model DevelopmentAI Methodologies ExpertiseExperience With PyTorchCloud Computing Platforms

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Large Deep Learning ModelsTraining OptimizationSelf-Supervised LearningRobustnessExplainabilityRLHFModel SparsificationQuantizationGradient CheckpointingModel Compression

Soft Skills

CollaborationResearch Agenda OwnershipAutonomous Project Management

Tools & Technologies

AWS UltraclustersHugging FaceLightningOpen-Source Tools

Industry Keywords

Applied ResearchNatural Language ProcessingMachine LearningAI Foundation ModelsPublications In AI Conferences

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 customer experiences
Translate complex research and technical work into tangible business goals
Collaborate with senior researchers to prepare 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; alternatively, an M.S. in these fields plus 2 years of experience in Applied Research
Exception: required degree may be obtained on or before the scheduled start date
Deep understanding 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 a related field
Preferred: LLM PhD focus on NLP or Master's with 5 years of industrial NLP research experience
Preferred: Publications related to pre-training large language models, deep learning theory, ACL, NAACL, EMNLP, NeurIPS, ICML, or ICLR
Preferred: Experience training a large language model from scratch with 10B+ parameters and 500B+ tokens
Preferred: Optimization experience involving 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 algorithms and/or optimizer design
Preferred: Experience with compiler design
Preferred: Finetuning PhD focused on guiding LLMs with further tasks
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 consideration for a new qualified applicant
Reasonable accommodations for applicants who require them