Job Details

Overview

At Capital One, we are creating trustworthy and reliable AI systems, changing banking for good. For years, Capital One has led 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 bring 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. At Capital One, you will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses.

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 and flex your interpersonal skills.

The Ideal Candidate

• You love the process of analyzing and creating, but also share our passion to do the right thing. You know at the end of the day it's about making the right decision for our customers.
• Innovative. You continually research and evaluate emerging technologies, staying current on state‑of‑the‑art methods, technologies, and applications and seeking opportunities to apply them.
• Creative. You thrive on bringing definition to big, undefined problems, asking questions, and pushing hard to find answers. You're not afraid to share a new idea.
• A leader. You challenge conventional thinking and work with stakeholders to identify and improve the status quo. You're passionate about talent development for your own team and beyond.
• Technical. You're comfortable with open‑source languages and passionate about developing further. You have hands‑on experience developing AI foundation models and solutions using open‑source tools and cloud computing platforms.
• Has a deep understanding of the foundations of AI methodologies.
• Experience building large deep‑learning models, whether on language, images, events, or graphs, as well as expertise in one or more of the following: training optimization, self‑supervised learning, robustness, explainability, RLHF.
• An engineering mindset as shown by a track record of delivering models at scale both in terms of training data and inference volumes.
• Experience in delivering libraries, platform‑level code, or solution‑level code to existing products.
• A professional with a track record of coming up with new ideas or improving upon existing ideas in machine learning, demonstrated by accomplishments such as first‑author publications or projects.
• Possess the ability to own and pursue a research agenda, including choosing impactful research problems and autonomously carrying out long‑running projects.

Basic Qualifications

• Currently has, or is in the process of obtaining, PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields, with an exception that required degree will be obtained on or before the scheduled start date plus 2 years of experience in Applied Research.

Preferred Qualifications

• PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering or related fields.
• LLM (PhD focus on NLP or Masters with 5 years of industrial NLP research experience).
• Multiple publications on pre‑training of large language models (e.g., technical reports, SSL techniques, pre‑training optimization).
• Member of a team that has trained a large language model from scratch (10B+ parameters, 500B+ tokens).
• Publications in deep learning theory at top conferences such as ACL, NAACL, EMNLP, NeurIPS, ICML, or ICLR.
• PhD focusing on geometric deep learning (Graph Neural Networks, Sequential Models, Multivariate Time Series).
• Experience scaling graph models to greater than 50M nodes and building large‑scale deep‑learning recommender systems.
• Experience optimizing training for very large deep‑learning models, including model sparsification, quantization, training parallelism, checkpointing, and compression.
• PhD focused on guiding LLMs with further tasks—supervised fine‑tuning, instruction‑tuning, dialogue‑fine‑tuning, parameter tuning—and deploying a fine‑tuned large language model.

Equal Opportunity Employment

Capital One is an equal‑opportunity employer (EOE, including disability/vet). We consider sponsorship for qualified applicants.