Applied Researcher 5 (AI Foundations: Evaluation and Agentic Workflows)

Capital One • McLean, VA

2d • Hybrid

About The Position

Capital One’s AI Foundations team develops trustworthy and reliable AI systems, working across the research life cycle from academic partnerships to production systems. The team collaborates with product, technology, and business leaders to apply state-of-the-art AI to Capital One’s business and create AI-powered customer experiences.

The Applied Researcher partners with data scientists, software engineers, machine learning engineers, and product managers to develop AI products. The role involves using technologies such as PyTorch, AWS Ultraclusters, Hugging Face, and Lightning to work with large volumes of numeric and textual data; building AI foundation models from design through training, evaluation, validation, and implementation; and conducting applied research that brings emerging AI developments into customer experiences. The researcher also communicates research in terms of business goals and leads cross-functional research threads that connect prototype development with deployment and production impact.

This is a hybrid position. The employee is expected to follow Capital One’s hybrid policy for required on-site presence at their designated office. The role is eligible for performance-based incentive compensation, which may include cash bonuses and/or long-term incentives.

Requirements

• Currently hold or be in the process of obtaining a Ph.D. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or a related field, with the required degree obtained on or before the scheduled start date, plus 2 years of experience in Applied Research; or hold an M.S. in one of those fields or a related field plus 4 years of experience in Applied Research.
• Hands-on experience developing AI foundation models and solutions using open-source tools and cloud computing platforms.
• Deep understanding of the foundations of AI methodologies.
• Experience building large deep learning models for language, images, events, or graphs, and expertise in at least one of training optimization, self-supervised learning, robustness, explainability, or RLHF.
• Engineering mindset demonstrated by a 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 producing high-quality machine learning ideas or improving existing ideas, 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.

Nice To Haves

• Ph.D. in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering, or a related field.
• Ph.D. focus on behavioral models, including geometric deep learning, graph neural networks, sequential models, or multivariate time series.
• Multiple papers on training models for graph and sequential data structures at KDD, ICML, NeurIPS, or ICLR.
• Experience scaling graph models to more than 50 million nodes.
• Experience with large-scale deep learning-based recommender systems.
• Experience in production real-time and streaming environments.
• Contributions to common open-source frameworks such as PyTorch Geometric or DGL.
• Proposed new methods for inference or representation learning on graphs or sequences.
• Experience working with datasets containing more than 100 million users.
• Proven ability to design and lead small-scale research initiatives, guiding junior researchers or engineers through experimental design and evaluation.

Responsibilities

• Partner with data scientists, software engineers, machine learning engineers, and product managers to deliver AI-powered products.
• Use AI and machine learning technologies to uncover insights in large volumes of numeric and textual data.
• Build AI foundation models through design, training, evaluation, validation, and implementation.
• Conduct applied research to bring emerging AI developments into customer experiences.
• Translate complex research into tangible business goals.
• Lead cross-functional research threads that bridge prototype model development and deployment with machine learning engineering partners.
• Ensure scientific insights translate into production impact.
• Research and evaluate emerging technologies and identify opportunities to apply state-of-the-art methods.
• Own and pursue a research agenda, selecting impactful research problems and carrying out long-running projects.

Benefits

• Comprehensive, competitive, and inclusive health, financial, and other benefits supporting total well-being.
• Eligibility for performance-based incentive compensation, which may include cash bonuses and/or long-term incentives.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree