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Capital One

Issues credit cards and runs a consumer bank

Applied Researcher 4

Natural Language Processing & LLMsConfirmed live in the last 24 hours
Full-timeIn-person

About the Job

Requirements

  • Currently has or is in the process of obtaining a PhD in Electrical Engineering, Computer Engineering, Computer Science, artificial intelligence, Mathematics, or a related field; alternatively, an M.S. in one of those fields plus two years of experience in Applied Research.
  • Has hands-on experience developing AI foundation models and solutions using open-source tools and cloud computing platforms.
  • Has a deep understanding of the foundations of artificial intelligence methodologies.
  • Has 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 from human feedback.
  • Has an engineering mindset demonstrated by delivering models at scale in terms of training data and inference volumes.
  • Has experience delivering libraries, platform-level code, or solution-level code to existing products.
  • Has a track record of producing high-quality ideas or improving existing machine learning ideas, demonstrated by accomplishments such as first-author publications or projects.
  • Can own and pursue a research agenda, including selecting impactful research problems and independently carrying out long-running projects.
  • For the LLM specialization: PhD focused on natural language processing or a Master's degree with five years of industrial natural language processing research experience; multiple publications on large language model pretraining topics; membership on a team that trained a large language model from scratch with 10B+ parameters and 500B+ tokens; publications in deep learning theory; and publications at ACL, NAACL, EMNLP, NeurIPS, ICML, or ICLR.
  • For the optimization (training and inference) specialization: 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; and experience with compiler design.
  • For the fine-tuning specialization: PhD focused on guiding large language models 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; and experience deploying a fine-tuned large language model.

Responsibilities

  • Partners with a cross-functional team of data scientists, software engineers, machine learning engineers, and product managers to deliver AI-powered products.
  • Uses technologies including PyTorch, AWS Ultraclusters, Hugging Face, and Lightning to uncover insights in large volumes of numeric and textual data.
  • Builds AI foundation models through design, training, evaluation, validation, and implementation.
  • Conducts applied research to advance AI developments into customer experiences.
  • Translates complex work into tangible business goals.
  • Collaborates with senior researchers to prepare internal technical reports or conference submissions summarizing novel methods or findings.
  • Researches and evaluates emerging technologies, stays current on published state-of-the-art methods, technologies, and applications, and identifies opportunities to apply them.
  • Works with stakeholders to identify and improve the status quo.

Desired Qualifications

  • Preferred PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering, or a related field.
  • For LLM specialization: PhD focused on natural language processing or a Master's degree with five years of industrial natural language processing research experience; multiple publications on large language model pretraining topics; membership on a team that trained a large language model from scratch with 10B+ parameters and 500B+ tokens; publications in deep learning theory; and publications at ACL, NAACL, EMNLP, NeurIPS, ICML, or ICLR.
  • For optimization (training and inference) specialization: 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; and experience with compiler design.
  • For fine-tuning specialization: PhD focused on guiding large language models 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; and experience deploying a fine-tuned large language model.

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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