Applied Researcher 4 (AI Foundations, LLM Core and Agentic AI)

Capital One • San Jose, CA

3d

About The Position

Capital One’s AI Foundations team develops trustworthy and reliable AI systems, spanning the research life cycle from academic partnerships to production systems. The team works 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 deliver AI-powered products. The role uses technologies including PyTorch, AWS Ultraclusters, Hugging Face, and Lightning to work with large volumes of numeric and textual data. It involves developing AI foundation models through design, training, evaluation, validation, and implementation; conducting applied research; communicating research in terms of business goals; and collaborating on internal technical reports and conference submissions.

The ideal candidate brings deep AI methodology knowledge, hands-on experience developing foundation models with open-source tools and cloud platforms, and an engineering mindset focused on delivering models at scale. The position requires the ability to identify meaningful research problems, pursue a research agenda, and carry out long-running projects independently. The role is eligible for performance-based incentive compensation, which may include cash bonuses and/or long-term incentives.

Requirements

• Currently has or is pursuing a PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or a related field; the degree must be obtained by the scheduled start date.
• Alternatively, has an M.S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or a related field and at least 2 years of Applied Research experience.
• Deep understanding of the foundations of AI methodologies.
• Experience building large deep learning models in areas such as language, images, events, or graphs.
• Expertise in one or more of training optimization, self-supervised learning, robustness, explainability, or RLHF.
• Track record delivering models at scale in terms of both training data and inference volumes.
• Experience delivering libraries, platform-level code, or solution-level code to existing products.
• Track record of high-quality machine learning ideas or improvements, demonstrated through accomplishments such as first-author publications or projects.
• Ability to own and pursue a research agenda, including selecting impactful problems and independently carrying out long-running projects.
• Hands-on experience developing AI foundation models and solutions using open-source tools and cloud computing platforms.

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 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.
• PhD 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 algorithmic and/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 such as PyTorch, AWS Ultraclusters, Hugging Face, and Lightning to uncover insights in large numeric and textual datasets.
• Build AI foundation models through design, training, evaluation, validation, and implementation.
• Conduct applied research that applies emerging AI developments to customer experiences.
• Translate complex research and technical work into tangible business goals.
• Collaborate with senior researchers on internal technical reports or conference submissions describing novel methods or findings.
• Identify impactful research problems, set a research agenda, and autonomously carry out long-running projects.

Benefits

• Comprehensive, competitive, and inclusive 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