Distinguished Applied Researcher

Capital One Capital One · Banking · McLean, VA +4

Distinguished Applied Researcher role focused on building AI foundation models from design through training, evaluation, validation, and implementation, and engaging in high-impact applied research to develop next-generation customer experiences. The role involves partnering with cross-functional teams and leveraging technologies like PyTorch, AWS, Huggingface, and VectorDBs. Requires a PhD or MS with significant experience in applied research, with a focus on large deep learning models, training optimization, self-supervised learning, robustness, explainability, or RLHF. Experience training large language models from scratch or through continued pre-training is highly preferred.

What you'd actually do

  1. Partner with a cross-functional team of data scientists, software engineers, machine learning engineers and product managers to deliver AI-powered products that change how customers interact with their money.
  2. Build AI foundation models through all phases of development, from design through training, evaluation, validation, and implementation.
  3. Engage in high impact applied research to take the latest AI developments and push them into the next generation of customer experiences.
  4. Leverage a broad stack of technologies — Pytorch, AWS Ultraclusters, Huggingface, Lightning, VectorDBs, and more — to reveal the insights hidden within huge volumes of numeric and textual data.
  5. Flex your interpersonal skills to translate the complexity of your work into tangible business goals.

Skills

Required

  • PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 4 years of experience in Applied Research or M.S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 6 years of experience in Applied Research
  • Deep understanding of the foundations of AI methodologies
  • Experience building large deep learning models (language, images, events, or graphs)
  • Expertise in training optimization, self-supervised learning, robustness, explainability, or RLHF
  • Track record of delivering models at scale (training data and inference volumes)
  • Experience in delivering libraries, platform level code or solution level code to existing products
  • Track record of coming up with new ideas or improving upon existing ideas in machine learning (e.g., first author publications or projects)
  • Ability to own and pursue a research agenda, including choosing impactful research problems and autonomously carrying out long-running projects

Nice to have

  • LLM
  • PhD focus on NLP or Masters with 10 years of industrial NLP research experience
  • Core contributor to team that has trained a large language model from scratch (10B + parameters, 500B+ tokens) or through continued pre-training, post t

What the JD emphasized

  • Core contributor to team that has trained a large language model from scratch (10B + parameters, 500B+ tokens) or through continued pre-training, post t
  • An engineering mindset as shown by a track record of delivering models at scale both in terms of training data and inference volumes.
  • A professional with a track record of coming up with new ideas or improving upon existing ideas in machine learning, demonstrated by accomplishments such as first author publications or projects.
  • Possess the ability to own and pursue a research agenda, including choosing impactful research problems and autonomously carrying out long-running projects.

Other signals

  • building AI foundation models
  • high impact applied research
  • delivering models at scale