Applied Researcher 5 (AI Foundations)

Capital One • San Jose, CA

2d • $262,500 - $326,800

About The Position

Capital One’s AI Foundations team develops trustworthy and reliable AI systems and applies state-of-the-art AI research to customer and business products. The team works across the research life cycle, from academic partnerships to production systems, in collaboration with product, technology, and business leaders.

The Applied Researcher partners with data scientists, software engineers, machine learning engineers, and product managers to build AI-powered products. The role involves developing AI foundation models from design through training, evaluation, validation, and implementation; conducting applied research to advance customer experiences; and leading cross-functional research efforts that connect prototype development with production deployment. The researcher translates technical work into business goals and uses technologies such as PyTorch, AWS Ultraclusters, Hugging Face, and Lightning to work with numeric and textual data.

The ideal candidate is an innovative, collaborative technical leader with a strong grasp of AI methodologies, hands-on experience building large deep learning models, and an engineering mindset for delivering models at scale. Relevant areas of expertise include training optimization, self-supervised learning, robustness, explainability, and RLHF. The role also calls for a record of impactful machine learning ideas or projects, the ability to deliver libraries or production code, and the ability to independently own a research agenda and long-running projects.

The position requires a PhD in a relevant field (or a qualifying master’s degree and applied research experience). Preferred areas include large language model pretraining, training and inference optimization, and fine-tuning. Capital One will consider sponsoring a new qualified applicant for employment authorization. The role is full-time and eligible for performance-based incentive compensation. The stated annual salary ranges from $262,500 to $299,600 in Cambridge, MA and McLean, VA, and from $286,400 to $326,800 in New York, NY and San Jose, CA; pay for other locations depends on the applicable location range and is confirmed in the offer letter. Capital One offers health, financial, and other benefits, with eligibility varying by employment status and management level.

Requirements

• Currently hold or be in the process of obtaining a PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or a related field, with the required degree obtained by the scheduled start date; alternatively, a relevant master's degree plus 4 years of experience in applied research.
• Have a deep understanding of AI methodology foundations.
• Have experience building large deep learning models in areas such as language, images, events, or graphs.
• Have expertise in one or more of training optimization, self-supervised learning, robustness, explainability, or RLHF.
• Demonstrate an engineering mindset and a track record of delivering models at scale in training data and inference volumes.
• Have experience delivering libraries, platform-level code, or solution-level code to existing products.
• Have a track record of high-quality machine learning ideas or improvements, demonstrated through accomplishments such as first-author publications or projects.
• Be able to independently choose impactful research problems and carry out long-running research projects.

Nice To Haves

• PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering, or a related field.
• For LLM research: PhD focus in NLP or a master’s degree with 5 years of industrial NLP research experience.
• Multiple publications related to large language model pretraining, such as pretrained LLM technical reports, self-supervised learning techniques, or pretraining optimization.
• Experience as a member of a team that trained a language model from scratch with 10B+ parameters and 500B+ tokens.
• Publications in deep learning theory.
• Publications at ACL, NAACL, EMNLP, NeurIPS, ICML, or ICLR.
• For training and inference optimization: PhD focused on optimizing the training of very large deep learning models.
• Multiple years of experience or publications in model sparsification, quantization, training parallelism or partitioning design, gradient checkpointing, or model compression.
• Experience optimizing training for a 10B+ model.
• Deep knowledge of deep learning algorithm or optimizer design.
• Experience with compiler design.
• For fine-tuning: PhD focused on guiding LLMs through supervised fine-tuning, instruction tuning, dialogue fine-tuning, or parameter tuning.
• Knowledge of transfer learning, model adaptation, and model guidance principles.
• Experience deploying a fine-tuned large language model.

Responsibilities

• Partner with data scientists, software engineers, machine learning engineers, and product managers to deliver AI-powered customer products.
• Use AI and machine learning technologies to uncover insights in large volumes of numeric and textual data.
• Develop AI foundation models through design, training, evaluation, validation, and implementation.
• Conduct applied research that advances AI capabilities and customer experiences.
• Translate technical work into tangible business goals.
• Lead cross-functional research efforts that connect prototype model development and deployment with ML engineering partners.
• Own and pursue a research agenda, including selecting impactful problems and carrying out long-running projects.

Benefits

• Comprehensive health, financial, and other benefits supporting total well-being.
• Performance-based incentive compensation, which may include cash bonuses and/or long-term incentives.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree