Applied Researcher II (AI Foundations)

Overview

At Capital One, we are creating trustworthy and reliable AI systems, changing banking for good. For years, Capital One has been leading the industry in using machine learning to create real‑time, intelligent, automated customer experiences. From informing customers about unusual charges to answering their questions in real time, our applications of AI & ML are bringing humanity and simplicity to banking. We are committed to building world‑class applied science and engineering teams and continue our industry‑leading capabilities with breakthrough product experiences and scalable, high‑performance AI infrastructure.

Team Description

The AI Foundations team is at the center of bringing our vision for AI at Capital One to life. Our work touches every aspect of the research life cycle, from partnering with academia to building production systems. We work with product, technology and business leaders to apply the state of the art in AI to our business.

Responsibilities

• Partner with a cross‑functional team of data scientists, software engineers, machine learning engineers and product managers to deliver AI‑powered products that change how customers interact with their money.
• Leverage a broad stack of technologies — PyTorch, AWS Ultraclusters, HuggingFace, Lightning, VectorDBs, and more — to reveal insights hidden within huge volumes of numeric and textual data.
• Build AI foundation models through all phases of development, from design through training, evaluation, validation, and implementation.
• Engage in high‑impact applied research to take the latest AI developments and push them into the next generation of customer experiences.
• Translate the complexity of your work into tangible business goals.

Ideal Candidate

• Passionate about analyzing, creating, and doing the right thing for customers.
• Innovative: continuously researches and evaluates emerging technologies, stays current on state‑of‑the‑art methods, and seeks opportunities to apply them.
• Creative: thrives on defining big, undefined problems, asks probing questions, and shares new ideas.
• Leader: challenges conventional thinking, works with stakeholders to improve the status quo, and is passionate about talent development.
• Technical: comfortable with open‑source languages; experienced in developing AI foundation models and solutions using open‑source tools and cloud platforms; deep understanding of AI methodologies; experience building large deep‑learning models (language, images, events, or graphs) with expertise in training optimization, self‑supervised learning, robustness, explainability, or RLHF; engineering mindset demonstrated by delivering models at scale (both training data and inference volumes); experience delivering libraries, platform‑level code, or solution‑level code to existing products; track record of new ideas or improvements evidenced by first‑author publications or projects; ability to own and pursue a research agenda.

Basic Qualifications

• Currently has, or is in the process of obtaining, a Ph.D. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or a related field (with the degree to be obtained on or before the scheduled start date) **and** 2 years of experience in Applied Research; or
• M.S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or a related field **and** 4 years of experience in Applied Research.

Preferred Qualifications

• Ph.D. in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering, or a related field.
• Behavioral Models: Ph.D. focus on geometric deep learning (graph neural networks, sequential models, multivariate time series); multiple papers at KDD, ICML, NeurIPS, ICLR; experience scaling graph models to >50 M nodes; experience with large‑scale deep‑learning recommender systems; production real‑time and streaming experience; contributions to open‑source frameworks (e.g., pytorch‑geometric, DGL); proposed new inference or representation‑learning methods for graphs or sequences; worked with datasets containing 100 M+ users.
• Finetuning: Ph.D. focus on guiding large language models (supervised finetuning, instruction‑tuning, dialogue‑finetuning, parameter tuning); demonstrated knowledge of transfer learning, model adaptation, and model guidance; experience deploying a fine‑tuned LLM.
• Data Preparation: Publications on tokenization, data quality, dataset curation, or labeling; contributions to a major open‑source corpus; contributions to open‑source libraries for data quality, dataset curation, or labeling.

Pay

Full‑time annual salary ranges (by location):

• Cambridge, MA: $262,500 – $299,600
• McLean, VA: $262,500 – $299,600
• New York, NY: $286,400 – $326,800
• San Francisco, CA: $286,400 – $326,800
• San Jose, CA: $286,400 – $326,800

Candidates hired in other locations will receive the pay range associated with that location. The role is also eligible for performance‑based incentive compensation, which may include cash bonuses and/or long‑term incentives.

Benefits

Capital One offers a comprehensive, competitive, and inclusive set of health, financial, and other benefits that support total well‑being. Eligibility varies based on full‑ or part‑time status, exempt or non‑exempt status, and management level.