Applied Researcher I (AI Foundations, Recommendation Systems, Personalization, Reinforcement Learning)

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 & 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. You will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses.

About the Team

The AI Foundations team is at the center of bringing our vision for AI at Capital One to life. Our work spans the entire research life cycle, from partnering with academia to building production systems. We collaborate with product, technology, and business leaders to apply state-of-the-art 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, Hugging Face, Lightning, VectorDBs, and more—to reveal insights hidden within large volumes of numeric and textual data.
• Build AI foundation models through all phases of development: design, training, evaluation, validation, and implementation.
• Engage in high-impact applied research to integrate the latest AI developments into next-generation customer experiences.
• Translate the complexity of your work into tangible business goals using strong interpersonal skills.

Qualifications

The ideal candidate:

• Loves the process of analyzing and creating while sharing our passion for doing the right thing for customers.
• Is innovative, continually researching and evaluating emerging technologies, staying current on state-of-the-art methods, and seeking opportunities to apply them.
• Is creative, thrives on defining big, undefined problems, asks questions, and pushes hard to find answers.
• Is a leader, challenges conventional thinking, works with stakeholders to improve the status quo, and is passionate about talent development.
• Is technical, comfortable with open-source languages, and passionate about further development. Has hands-on experience developing AI foundation models using open-source tools and cloud computing platforms.
• Has a deep understanding of AI methodologies, experience building large deep learning models (language, images, events, or graphs), and expertise in one or more of the following: training optimization, self-supervised learning, robustness, explainability, RLHF.
• Demonstrates an engineering mindset with a track record of delivering models at scale in both training data and inference volumes.
• Has experience delivering libraries, platform-level code, or solution-level code to existing products.
• Has a professional track record of high-quality ideas or improvements in machine learning, demonstrated by first-author publications or projects.
• Can own and pursue a research agenda, including choosing impactful problems and autonomously carrying out long-running projects.

Requirements

• Currently has, or is in the process of obtaining, a PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields (required degree must be obtained on or before the scheduled start date).
• Alternatively, 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 related fields.
• PhD focus on NLP or a Master’s with 5 years of industrial NLP research experience.
• Multiple publications on topics related to pre-training 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 (10B+ parameters, 500B+ tokens).
• Publications in deep learning theory or at conferences such as ACL, NAACL, EMNLP, NeurIPS, ICML, or ICLR.
• PhD focused on guiding LLMs with further tasks (Supervised Fine-tuning, Instruction-Tuning, Dialogue-Fine-tuning, Parameter Tuning).
• Demonstrated knowledge of principles of transfer learning, model adaptation, and model guidance.
• Experience deploying a fine-tuned large language model.
• Publications studying tokenization, data quality, dataset curation, or labeling.
• Contributions to major open-source corpora or libraries for data quality, dataset curation, or labeling.

Pay

The annual full-time salary ranges for this role by location are:

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

Candidates hired in other locations will be subject to the pay range associated with their location. Part-time salaries will be prorated based on agreed-upon hours. 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.