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

About the Role

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.

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

Qualifications

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 (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 related fields.
• PhD focus on NLP or Master’s 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 (10B+ parameters, 500B+ tokens).
• Publications in deep learning theory or at conferences such as ACL, NAACL, EMNLP, NeurIPS, ICML, or ICLR.
• PhD focused on topics related to 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.
• Contribution to a major open-source corpus or open-source libraries for data quality, dataset curation, or labeling.

Skills

• Innovative: Continually researches and evaluates emerging technologies, stays current on published state-of-the-art methods, and seeks opportunities to apply them.
• Creative: Thrives on defining big, undefined problems, asks questions, and pushes hard to find answers.
• Leadership: Challenges conventional thinking, works with stakeholders to improve the status quo, and is passionate about talent development.
• Technical: Comfortable with open-source languages and passionate about further development. Hands-on experience developing AI foundation models using open-source tools and cloud computing platforms.
• Deep understanding of AI methodologies, including building large deep learning models (language, images, events, or graphs) and expertise in training optimization, self-supervised learning, robustness, explainability, or RLHF.
• Engineering mindset with a track record of delivering models at scale in terms of 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 in machine learning, demonstrated by first-author publications or projects.
• Ability to own and pursue a research agenda, including choosing impactful problems and autonomously carrying out long-running projects.

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 that location. Part-time salaries will be prorated. 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 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.