Applied Researcher 4 (AI Foundations: Recommendation Systems)

Capital One • McLean, VA

5d

About The Position

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

The Applied Researcher partners with data scientists, software engineers, machine learning engineers, and product managers to build AI-powered products. The role involves using technologies such as PyTorch, AWS Ultraclusters, Hugging Face, and Lightning to analyze large volumes of numeric and textual data, and developing AI foundation models from design through training, evaluation, validation, and implementation. The researcher conducts applied research that advances customer experiences, translates technical work into business goals, and collaborates with senior researchers on internal technical reports or conference submissions.

The ideal candidate has hands-on experience developing foundation models and AI solutions with open-source tools and cloud platforms, a deep understanding of AI methodologies, and a record of delivering models and code at scale. The role also calls for the ability to set and pursue an independent research agenda and to produce or improve high-quality machine-learning ideas.

Requirements

• Currently hold or be 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, hold an M.S. in one of these or a related field and have at least 2 years of applied research experience.
• 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 ones, demonstrated through accomplishments such as first-author publications or projects.
• Ability to own and pursue a research agenda, select impactful research problems, and carry out long-running projects autonomously.

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 on large language model pre-training, 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 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 through further tasks such as supervised fine-tuning, instruction tuning, dialogue fine-tuning, or parameter tuning.
• Knowledge of transfer learning, model adaptation, and model guidance.
• Experience deploying a fine-tuned large language model.
• Ability to reproduce and extend peer-reviewed AI research using modern open-source frameworks.
• Experience designing controlled experiments and documenting reproducibility results for internal or public research.

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 and apply emerging AI developments to customer experiences.
• Translate complex technical work into tangible business goals.
• Collaborate with senior researchers to prepare internal technical reports or conference submissions.
• Choose impactful research problems and autonomously pursue long-running research projects.

Benefits

• Comprehensive health, financial, and other benefits supporting total well-being.
• Eligibility to earn 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