Applied Researcher 4 (AI Foundations - LLM, Optimization and Finetuning)

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, 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 for stakeholders.
• Collaborate with senior researchers to prepare internal tech reports or conference submissions summarizing novel methods or findings.

Ideal Candidate

• Passionate about making the right decisions for customers and doing the right thing.
• 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 pushes hard to find answers.
• Leader: challenges conventional thinking, works with stakeholders to improve the status quo, and is passionate about talent development.
• Technical: comfortable with open‑source languages, hands‑on experience developing AI foundation models using open‑source tools and cloud platforms, and deep understanding of AI methodologies.
• Experienced 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) and shipping libraries or platform‑level code to existing products.
• Proven track record of high‑quality ideas or improvements in machine learning, evidenced by first‑author publications or notable projects.
• Ability to own and pursue a research agenda, selecting impactful problems and autonomously executing long‑running projects.

Basic Qualifications

• PhD (or in‑process) in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related field (degree to be obtained by start date) or M.S. in one of those fields plus 2 years of applied research experience.

Preferred Qualifications

• PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering, or related field.
• LLM‑focused PhD on NLP or a Master’s with 5+ years of industrial NLP research experience.
• Multiple publications on pre‑training large language models, including technical reports, SSL techniques, or model‑pre‑training optimization.
• Member of a team that has trained a large language model from scratch (10 B+ parameters, 500 B+ tokens).
• Publications in deep‑learning theory and top conferences (ACL, NAACL, EMNLP, NeurIPS, ICML, ICLR).
• Expertise in optimizing training and inference of very large models: model sparsification, quantization, training parallelism/partitioning, gradient checkpointing, model compression, compiler design.
• Experience optimizing training for a 10 B+ model.
• Deep knowledge of deep‑learning algorithmic and optimizer design.
• Experience guiding LLMs through supervised fine‑tuning, instruction‑tuning, dialogue‑fine‑tuning, or parameter tuning; demonstrated knowledge of transfer learning and model adaptation.
• Experience deploying a fine‑tuned large language model.

Pay

• Cambridge, MA: $218,700 – $249,600 (Applied Researcher 4)
• McLean, VA: $218,700 – $249,600 (Applied Researcher 4)
• New York, NY: $238,600 – $272,300 (Applied Researcher 4)
• San Jose, CA: $238,600 – $272,300 (Applied Researcher 4)
• Other locations: salary range based on local market; actual annualized salary reflected in offer letter.
• Eligible for performance‑based incentive compensation, including cash bonuses and/or long‑term incentives.

Benefits

• Comprehensive health, financial, and other benefits supporting total well‑being.
• Eligibility varies based on full‑ or part‑time status, exempt or non‑exempt status, and management level.
• Capital One offers a competitive, inclusive benefits package detailed on the Capital One Careers website.