Applied Researcher 4 (AI Foundations)

Capital One • San Jose, CA

2d

About The Position

The AI Foundations team at Capital One works across the AI research life cycle, from academic partnerships to production systems, applying state-of-the-art AI in collaboration with product, technology, and business leaders. The Applied Researcher will help develop trustworthy and reliable AI systems and customer experiences, contributing to applied science and engineering capabilities and scalable, high-performance AI infrastructure.

The role partners with data scientists, software engineers, machine learning engineers, and product managers to build AI-powered products. It uses technologies such as PyTorch, AWS Ultraclusters, Hugging Face, and Lightning to work with large volumes of numerical and textual data. The researcher builds AI foundation models across their development lifecycle—including design, training, evaluation, validation, and implementation—and applies emerging AI research to customer experiences. The role also translates technical work into business goals and works with senior researchers on internal technical reports or conference submissions.

The successful candidate brings hands-on experience developing foundation models and AI solutions with open-source tools and cloud platforms, a deep understanding of AI methodologies, and the ability to pursue an independent research agenda. Relevant areas may include large deep learning models and training optimization, self-supervised learning, robustness, explainability, or RLHF. The role is based on a full-time salary range that varies by listed location and is eligible for performance-based incentive compensation.

Requirements

• Currently has or is pursuing a PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or a related field; the required degree must be obtained on or before the scheduled start date; or has an M.S. in one of these fields or a related field plus 2 years of Applied Research experience.
• Deep understanding of AI methodologies.
• Hands-on experience developing AI foundation models and solutions using open-source tools and cloud computing platforms.
• Experience building large deep learning models involving language, images, events, or graphs.
• Expertise in one or more of training optimization, self-supervised learning, robustness, explainability, or RLHF.
• 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 producing high-quality machine learning ideas or improving existing ideas, demonstrated through accomplishments such as first-author publications or projects.
• Ability to own and pursue a research agenda, choose impactful research problems, and autonomously carry out long-running projects.

Nice To Haves

• PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering, or a related field.
• PhD focused on NLP, or a master's degree plus 5 years of industrial NLP research experience.
• Multiple publications related to pre-training large language models, such as technical reports on pretrained LLMs, self-supervised learning techniques, or pre-training optimization.
• Membership on a team that trained a large language model from scratch with 10B+ parameters and 500B+ tokens.
• Publications in deep learning theory.
• Publications at ACL, NAACL, EMNLP, NeurIPS, ICML, or ICLR.
• PhD focused on optimizing 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 algorithms or optimizer design.
• Experience with compiler design.
• PhD focused on guiding LLMs with further tasks, such as supervised fine-tuning, instruction tuning, dialogue fine-tuning, or parameter tuning.
• Demonstrated knowledge of transfer learning, model adaptation, and model guidance.
• 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 products.
• Use technologies including PyTorch, AWS Ultraclusters, Hugging Face, and Lightning to uncover insights in large volumes of numerical and textual data.
• Build AI foundation models through design, training, evaluation, validation, and implementation.
• Conduct applied research and bring emerging AI developments into customer experiences.
• Translate complex technical work into tangible business goals.
• Collaborate with senior researchers to prepare internal technical reports or conference submissions summarizing novel methods or findings.
• Own and pursue a research agenda, including selecting impactful research problems and independently carrying out long-running projects.
• Develop models and deliver libraries, platform-level code, or solution-level code for existing products at scale.

Benefits

• Comprehensive health, financial, and other benefits supporting total well-being; eligibility varies by employment status and management level.
• 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