Applied Researcher I (AI Foundations, LLM Customization, Finetuning, Reinforcement Learning)

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. At Capital One, you will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses who have come to love the products and services we build.

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.
• Flex interpersonal skills to translate the complexity of your work into tangible business goals.

Ideal Candidate

• Innovative: Continuously research and evaluate emerging technologies; stay current on state‑of‑the‑art methods, technologies, and applications; seek opportunities to apply them.
• Creative: Thrive on defining big, undefined problems; ask questions; push hard to find answers; share new ideas.
• Leader: Challenge conventional thinking; work with stakeholders to improve the status quo; passionate about talent development for your own team and beyond.
• Technical: Comfortable with open‑source languages; hands‑on experience 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, RLHF; engineering mindset demonstrated by delivering models at scale (training data and inference volumes); experience delivering libraries, platform‑level code, or solution‑level code to existing products; track record of high‑quality ideas or improvements shown by first‑author publications or projects; ability to own and pursue a research agenda, choose impactful problems, and autonomously carry out long‑running projects.

Basic Qualifications

• Currently has, or is in the process of obtaining, a PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields (or the required degree will be obtained on or before the scheduled start date), or an M.S. in those fields 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 with an LLM focus on NLP, or a Master’s degree with 5 years of industrial NLP research experience.
• Multiple publications on topics related to the pre‑training of large language models (e.g., technical reports of pre‑trained LLMs, SSL techniques, 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; publications at ACL, NAACL, EMNLP, NeurIPS, ICML, or ICLR.
• Experience with fine‑tuning: PhD focus on guiding LLMs with further tasks (supervised fine‑tuning, instruction‑tuning, dialogue‑fine‑tuning, parameter tuning); demonstrated knowledge of transfer learning, model adaptation, and model guidance; experience deploying a fine‑tuned large language model.
• Data preparation expertise: publications studying tokenization, data quality, dataset curation, or labeling; contributions to a major open‑source corpus; contributions to open‑source libraries for data