Applied Researcher 5

Capital One • San Jose, CA

11h

About The Position

Capital One’s AI Foundations team develops trustworthy and reliable AI systems, connecting work across the research life cycle—from academic partnerships to production systems. The team collaborates with product, technology, and business leaders to apply state-of-the-art AI to the business and build scalable, high-performance AI infrastructure and customer experiences.

The Applied Researcher partners with data scientists, software engineers, machine learning engineers, and product managers to develop AI-powered products. The role builds AI foundation models through design, training, evaluation, validation, and implementation; conducts applied research to bring emerging AI developments into customer experiences; and leads research efforts that connect prototype development with deployment alongside ML engineering partners. The researcher also translates technical work into business goals and pursues research problems with potential for meaningful impact.

Requirements

• Currently hold or be pursuing a Ph.D. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or a related field, with the required degree obtained by the scheduled start date, plus 2 years of experience in Applied Research; or hold an M.S. in one of those fields or a related field plus 4 years of experience in Applied Research.
• Deep understanding of the foundations 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, and expertise in at least one 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 independently choose impactful research problems and carry out long-running research projects.

Nice To Haves

• Ph.D. in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering, or a related field.
• Ph.D. focus on NLP, or a master's degree with 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 model 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.
• Ph.D. focused on optimizing training of very large deep learning models.
• Multiple years of experience and/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 and/or optimizer design.
• Experience with compiler design.
• Ph.D. 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 analyze large volumes of numeric and textual data.
• Build AI foundation models through design, training, evaluation, validation, and implementation.
• Conduct applied research to bring emerging AI developments into customer experiences.
• Translate the complexity of research and technical work into tangible business goals.
• Lead cross-functional research threads 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.
• Eligible for 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