Applied Researcher I (AI Foundations)

At Capital One, we are creating trustworthy and reliable AI systems, changing banking for good. For years, we have 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 and ML bring humanity and simplicity to banking. We are committed to building world-class applied science and engineering teams, delivering breakthrough product experiences, and scaling high-performance AI infrastructure.

About the role

As part of the AI Foundations team, you will help bring our vision for AI at Capital One to life. Your work will touch every aspect of the research life cycle, from partnering with academia to building production systems. You will collaborate with product, technology, and business leaders to apply state-of-the-art AI to our business and deliver AI-powered products that transform how customers interact with their money.

You will leverage technologies such as PyTorch, AWS Ultraclusters, Hugging Face, Lightning, and VectorDBs to uncover insights from vast volumes of numeric and textual data. This role involves building AI foundation models through all phases of development: design, training, evaluation, validation, and implementation. You will also engage in high-impact applied research, pushing the latest AI advancements into next-generation customer experiences while translating complex work into tangible business goals.

Responsibilities

• Partner with cross-functional teams of data scientists, software engineers, machine learning engineers, and product managers to deliver AI-powered products.
• Build AI foundation models through design, training, evaluation, validation, and implementation.
• Conduct high-impact applied research to integrate the latest AI developments into customer experiences.
• Translate complex technical work into actionable business goals using strong interpersonal skills.
• Develop and optimize large deep learning models (language, images, events, or graphs) with expertise in areas such as training optimization, self-supervised learning, robustness, explainability, or RLHF.
• Deliver models at scale, handling both large training datasets and high inference volumes.
• Contribute to libraries, platform-level code, or solution-level integrations for existing products.
• Pursue and own a research agenda, including selecting impactful problems and autonomously executing long-term projects.

Qualifications

Basic Qualifications

• Currently has, or is in the process of obtaining, a PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or a related field (degree must be obtained on or before the scheduled start date).
• OR an M.S. in the same fields plus 2 years of experience in Applied Research.

Preferred Qualifications

• PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering, or a related field.
• Focus on NLP or a Master’s degree with 5+ years of industrial NLP research experience.
• Multiple publications on pre-training large language models (e.g., technical reports on pre-trained LLMs, SSL techniques, model 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 or at conferences such as ACL, NAACL, EMNLP, NeurIPS, ICML, or ICLR.
• Expertise in optimization (training and inference), including model sparsification, quantization, training parallelism/partitioning, gradient checkpointing, or model compression.
• Experience optimizing training for 10B+ parameter models and deep knowledge of deep learning algorithmic or optimizer design.
• Experience with compiler design.
• PhD focused on guiding LLMs with further tasks (e.g., supervised fine-tuning, instruction-tuning, dialogue-finetuning, parameter tuning).
• Demonstrated knowledge of transfer learning, model adaptation, and model guidance principles.
• Experience deploying a fine-tuned large language model.

Pay

The annual full-time salary ranges for this role are as follows (part-time salaries will be prorated):

• Cambridge, MA: $218,700 - $249,600
• McLean, VA: $218,700 - $249,600
• New York, NY: $238,600 - $272,300
• San Jose, CA: $238,600 - $272,300

Candidates hired in other locations will be subject to the pay range associated with their location. This role is also eligible for performance-based incentive compensation, including cash bonuses and/or long-term incentives (LTI).

Benefits

Capital One offers a comprehensive, competitive, and inclusive set of health, financial, and other benefits to support your total well-being. Eligibility varies based on full- or part-time status, exempt or non-exempt status, and management level. Learn more at the Capital One Careers website.