Applied Researcher 4

Capital One • San Jose, CA

1d • $238,600 - $272,300

About The Position

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

The Applied Researcher partners with data scientists, software engineers, machine learning engineers, and product managers to create AI-powered products. The role builds AI foundation models through design, training, evaluation, validation, and implementation, and conducts applied research to bring emerging AI advances into customer experiences. It also involves working with technologies such as PyTorch, AWS Ultraclusters, Hugging Face, and Lightning; translating technical work into business goals; and collaborating with senior researchers on internal technical reports or conference submissions.

The ideal candidate is technically strong, innovative, creative, collaborative, and committed to customer-focused decision-making. They have experience developing and delivering large-scale AI models and solutions, can pursue an independent research agenda, and have a record of high-quality research ideas or contributions. Capital One will consider sponsoring a new qualified applicant for employment authorization. The position is eligible for performance-based incentive compensation, which may include cash bonuses and/or long-term incentives.

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, hold an M.S. in one of these fields or a related field and have at least 2 years of Applied Research experience.
• Have a deep understanding of the foundations of AI methodologies.
• Have experience building large deep learning models for language, images, events, or graphs, and expertise in at least one of training optimization, self-supervised learning, robustness, explainability, or RLHF.
• Demonstrate an engineering mindset and a 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 record of high-quality machine-learning ideas or improvements, demonstrated by accomplishments such as first-author publications or projects.
• Be able to own and pursue a research agenda, select impactful research problems, and autonomously carry out long-running projects.
• Have 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 focus in 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, including 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 numeric and textual data.
• Build AI foundation models through design, training, evaluation, validation, and implementation.
• Conduct applied research that brings emerging AI developments into customer experiences.
• Translate complex technical work into tangible business goals.
• Collaborate with senior researchers on internal technical reports or conference submissions describing novel methods or findings.

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