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Capital One

Issues credit cards and runs a consumer bank

Senior Director - Applied Researcher

AI ResearchConfirmed live in the last 24 hours
Full-timeIn-person

About the Job

Requirements

  • Have a PhD in Electrical Engineering, Computer Engineering, Computer Science, artificial intelligence, Mathematics, or a related field plus six years of experience in Applied Research, or an M.S. in one of those fields plus eight years of experience in Applied Research.
  • Have at least five years of people leadership experience.
  • Have deep, first-principles understanding of the foundations of AI methodologies.
  • Have experience building large deep learning models for language, images, events, or graphs, and expertise in one or more of training optimization, self-supervised learning, robustness, explainability, or reinforcement learning.
  • Have experience building production-grade systems that deliver large-scale business impact.
  • Have an engineering mindset demonstrated by 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 track record of high-quality ideas or improving existing ideas in machine learning, demonstrated by accomplishments such as first-author publications or projects.
  • Be able to own and pursue a research agenda, choose 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.
  • Have a PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering, or related fields.
  • For LLM specialization: have a PhD focused on natural language processing or a master's degree plus ten years of industrial NLP research experience; be a core contributor to a team that trained a large language model from scratch with 10B+ parameters and 500B+ tokens; have numerous publications at ACL, NAACL, EMNLP, NeurIPS, ICML, or ICLR on large-language-model pretraining; have worked on an LLM currently available for use; be able to guide the technical direction of a large-scale model training team; have experience with clusters of 500+ GPU nodes and an LLM scaled to 70B parameters and 1T+ tokens; and have experience with DeepSpeed or NeMo training optimization frameworks.
  • For behavioral models specialization: have a PhD focused on geometric deep learning topics such as graph neural networks, sequential models, or multivariate time series; have technical leadership experience in deploying a very large user behavior model; have multiple papers on graph or sequential data structures at KDD, ICML, NeurIPS, or ICLR; have worked on graph models scaled to more than 50 million nodes and large-scale deep-learning recommender systems; have experience with production real-time and streaming environments; have contributed to PyTorch Geometric or DGL; have proposed new methods for inference or representation learning on graphs or sequences; and have worked with datasets containing more than 100 million users.
  • For training and inference optimization specialization: have a PhD focused on optimizing training of very large language models, or have five or more years of experience and/or publications in model sparsification, quantization, training parallelism or partitioning design, gradient checkpointing, or model compression.
  • For fine-tuning specialization: have a PhD focused on guiding LLMs with further tasks such as supervised fine-tuning, instruction tuning, dialogue fine-tuning, or parameter tuning; demonstrate knowledge of transfer learning, model adaptation, and model guidance; and have experience deploying a fine-tuned large language model.
  • For data-preparation specialization: have numerous publications studying tokenization, data quality, dataset curation, or labeling; have made leading contributions to one or more large open-source corpora of 1 trillion or more tokens; and have contributed to open-source libraries for data quality, dataset curation, or labeling.
  • Have a track record of scaling research organizations and establishing cross-disciplinary programs that translate cutting-edge AI research into enterprise impact.

Responsibilities

  • Partner with a cross-functional team of applied researchers, data scientists, AI engineers, machine learning engineers, and product managers to deliver AI-powered products.
  • Use technologies including PyTorch, AWS Ultraclusters, Hugging Face, and Lightning to analyze large volumes of numeric and textual data.
  • Build and apply AI foundation models through design, training, evaluation, validation, and implementation.
  • Conduct applied research to bring the latest AI developments into customer experiences.
  • Translate complex technical work into tangible business goals.
  • Set the organization's strategic research vision and roadmap, aligning long-term scientific discovery with business transformation priorities.
  • Lead teams and guide and mentor applied scientists.
  • Represent Capital One in the research community and collaborate with relevant AI research faculty.

Desired Qualifications

  • For LLM specialization: be a core contributor to a team that trained a large language model from scratch with 10B+ parameters and 500B+ tokens; have numerous publications at ACL, NAACL, EMNLP, NeurIPS, ICML, or ICLR on large-language-model pretraining; have worked on an LLM currently available for use; be able to guide the technical direction of a large-scale model training team; have experience with clusters of 500+ GPU nodes and an LLM scaled to 70B parameters and 1T+ tokens; and have experience with DeepSpeed or NeMo training optimization frameworks.
  • For behavioral models specialization: have a PhD focused on geometric deep learning topics such as graph neural networks, sequential models, or multivariate time series; have technical leadership experience in deploying a very large user behavior model; have multiple papers on graph or sequential data structures at KDD, ICML, NeurIPS, or ICLR; have worked on graph models scaled to more than 50 million nodes and large-scale deep-learning recommender systems; have experience with production real-time and streaming environments; have contributed to PyTorch Geometric or DGL; have proposed new methods for inference or representation learning on graphs or sequences; and have worked with datasets containing more than 100 million users.
  • For training and inference optimization specialization: have a PhD focused on optimizing training of very large language models, or have five or more years of experience and/or publications in model sparsification, quantization, training parallelism or partitioning design, gradient checkpointing, or model compression.
  • For fine-tuning specialization: have a PhD focused on guiding LLMs with further tasks such as supervised fine-tuning, instruction tuning, dialogue fine-tuning, or parameter tuning; demonstrate knowledge of transfer learning, model adaptation, and model guidance; and have experience deploying a fine-tuned large language model.
  • For data-preparation specialization: have numerous publications studying tokenization, data quality, dataset curation, or labeling; have made leading contributions to one or more large open-source corpora of 1 trillion or more tokens; and have contributed to open-source libraries for data quality, dataset curation, or labeling.

About the Company

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Capital One

Issues credit cards and runs a consumer bank

Capital One runs a consumer and commercial bank that issues credit cards, takes deposits, and finances cars and businesses. Its Venture travel cards and Quicksilver cash-back cards let customers earn rewards on everyday spending, while its online banking tools manage payments and savings. Customers use checking and savings accounts through an app, branches, and cash machines, and commercial clients use treasury tools to manage company cash. The bank earns interest on loans and fees from banking and card transactions, with customer deposits helping fund its lending operations. Capital One was founded in 1994 and has its headquarters in McLean, Virginia.

Company size
10,001+
Company stage
IPO
Headquarters
McLean, VA
Founded
1994

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