Applied Researcher 5 (AI Foundations)

Capital One • McLean, NY

22h

About The Position

Capital One’s AI Foundations team works across the AI research life cycle, from academic partnerships through production systems, applying state-of-the-art AI to the company’s business. The Applied Researcher partners with data scientists, software engineers, machine learning engineers, and product managers to develop AI-powered products and improve how customers interact with their money.

The role involves developing AI foundation models throughout their lifecycle, including design, training, evaluation, validation, and implementation. It applies current AI research to customer experiences and uses technologies such as PyTorch, AWS Ultraclusters, Hugging Face, and Lightning to work with large volumes of numeric and textual data. The researcher also leads cross-functional research efforts that connect prototype development with deployment, translating scientific findings into production impact and business goals.

The ideal candidate combines applied research expertise and an engineering mindset, can pursue an independent research agenda, and communicates effectively across teams. The role calls for experience developing large deep-learning models and delivering models, libraries, platforms, or solutions at scale. The company lists multiple possible research specializations, including large language model pre-training, training and inference optimization, and fine-tuning.

Requirements

• Currently has, or is 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 on or before the scheduled start date, plus 2 years of experience in Applied Research; alternatively, an M.S. in one of these fields or a related field plus 4 years of experience in Applied Research.
• Deep understanding of the foundations of AI methodologies.
• Experience building large deep-learning models, such as models for 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 high-quality machine-learning ideas or improvements, demonstrated by accomplishments such as first-author publications or projects.
• Ability to own and pursue a research agenda, including selecting impactful problems and autonomously 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.
• LLM PhD focus on NLP, or a master’s degree with 5 years of industrial NLP research experience.
• Multiple publications on large language model pre-training, 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 the 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 AI technologies and tools 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 and apply emerging AI developments to customer experiences.
• Translate complex research into tangible business goals.
• Lead cross-functional research efforts connecting prototype model development and deployment with ML engineering partners.
• Choose impactful research problems and independently pursue long-running research projects.
• Research and evaluate emerging technologies and seek opportunities to apply state-of-the-art methods.

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