Applied Researcher 5

Capital One • San Jose, CA

1d • $262,500 - $326,800

About The Position

Capital One’s AI Foundations team works across the AI research life cycle, from academic partnerships to production systems, collaborating with product, technology, and business leaders to apply state-of-the-art AI. The Applied Researcher 5 partners with data scientists, software engineers, machine learning engineers, and product managers to develop AI-powered products that improve how customers interact with their money.

The role develops AI foundation models through design, training, evaluation, validation, and implementation, and applies emerging AI research to create new customer experiences. It uses technologies including PyTorch, AWS Ultraclusters, Hugging Face, and Lightning to analyze large volumes of numeric and textual data. The researcher leads cross-functional research work that connects prototype model development with deployment in partnership with ML engineering teams, translating scientific findings into production impact and business goals.

The position calls for deep understanding of AI methodologies, hands-on experience building large deep learning models and AI foundation model solutions, and an engineering mindset for delivering models at scale. The successful candidate can own a research agenda, select impactful problems, carry out long-running projects, and communicate effectively with stakeholders.

Requirements

• Currently have 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 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 in areas such as 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 by accomplishments such as first-author publications or projects.
• Ability to independently choose impactful research problems and pursue long-running research 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 focus on geometric deep learning, including graph neural networks, sequential models, or multivariate time series.
• Multiple papers on training models on graph and sequential data structures at KDD, ICML, NeurIPS, or ICLR.
• Experience scaling graph models to more than 50 million nodes.
• Experience with large-scale deep-learning-based recommender systems.
• Experience with production real-time and streaming environments.
• Contributions to open-source frameworks such as PyTorch Geometric or DGL.
• Proposed new methods for inference or representation learning on graphs or sequences.
• Experience working with datasets containing 100 million or more users.
• PhD focus on optimizing training of very large language models.
• Five or more years of experience and/or publications in model sparsification, quantization, training parallelism or partitioning design, gradient checkpointing, or model compression.
• PhD focus on guiding large language models with 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.
• Publications studying tokenization, data quality, dataset curation, or labeling.
• Contribution to a major open-source corpus.
• Contribution to open-source libraries for data quality, dataset curation, or labeling.
• Ability to design and lead small-scale research initiatives and guide junior researchers or engineers through experimental design and evaluation.

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 analyze large volumes of numeric and textual data.
• Build AI foundation models through design, training, evaluation, validation, and implementation.
• Conduct applied research that translates emerging AI developments into customer experiences.
• Communicate complex work in terms of tangible business goals.
• Lead cross-functional research threads connecting prototype model development and deployment with ML engineering partners.
• Own and pursue a research agenda, including selecting impactful research problems and carrying out long-running projects.

Benefits

• Comprehensive, competitive, and inclusive health, financial, and other benefits supporting total well-being.
• Eligibility 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