Applied Researcher 5

Capital One

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

Posted 11/10/2026full-timeUnited StatesJuniorMid-Level💰 $262,500 - $326,800 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 applied research. Proven ability to translate complex AI concepts into actionable business strategies while leading 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, RLHFHands-On Experience Developing AI Foundation Models Using Open-Source ToolsExperience With Kubernetes, KubeRay, PyTorch Geometric, DGL, Hugging Face

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Deep LearningAI MethodologiesApplied ResearchModel TrainingModel EvaluationModel ValidationGenerative AIModel SparsificationQuantizationTraining Parallelism

Soft Skills

LeadershipCollaborationProblem-SolvingCommunicationResearch Agenda Ownership

Tools & Technologies

PyTorchAWS UltraclustersHugging FaceKubernetesKubeRayPyTorch GeometricDGL

Industry Keywords

Applied ResearchMachine LearningGenerative RecommendationLarge-Scale Semantic SearchReal-Time Streaming Environments

Tech Stack

Tools & technologies

AWSCloudKubernetesPyTorch

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 to advance AI developments into next-generation customer experiences
Translate complex research into tangible business goals
Lead cross-functional research threads bridging 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 degree obtained on or before the scheduled start date, plus 2 years of experience in Applied Research; OR M.S. in a related field plus 4 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 problems, and autonomously execute 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, High Performance Computing, Applied Mathematics, Electrical Engineering, or related fields
Preferred: LLM PhD focus on Generative AI or master's degree with 5 years of industrial deep-learning research experience
Preferred: Multiple publications at top-tier venues on generative recommendation, RAG, LLM-based agents, or large-scale semantic search
Preferred: Experience training large language models from scratch with 10B+ parameters and 500B+ tokens
Preferred: Behavioral Models PhD focus on geometric deep learning
Preferred: Papers on graph and sequential data structures at top-tier venues
Preferred: Experience training or adapting 10B+ parameter foundation models for complex financial data, transactional sequences, or user behavior graphs
Preferred: Experience scaling graph models beyond 50M nodes in real-time streaming environments
Preferred: Contributions to PyTorch Geometric or DGL
Preferred: New methods for inference or representation learning on graphs or sequences
Preferred: Experience with datasets exceeding 100M users
Preferred: Multiple years of experience or publications in model sparsification, quantization, training parallelism/partitioning, or gradient checkpointing for 10B+ parameter models
Preferred: Experience with Kubernetes, KubeRay, PyTorch Geometric, DGL, and Hugging Face
Preferred: Experience building and deploying deep-learning recommender systems at scale for 100M+ users or items
Preferred: Finetuning PhD focused on aligning LLMs for recommendation and search via SFT, RLHF, or DPO
Preferred: Experience deploying fine-tuned LLMs optimized for serving performance
Preferred: Ability to design and lead small-scale research initiatives and guide junior researchers or engineers
Capital One will consider sponsoring a new qualified applicant for employment authorization for this position

Benefits

Comp & perks

Performance-based incentive compensation, which may include cash bonuses 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