Applied Researcher I (ai Foundations, Recommendation Systems, Personalization, Reinforcement Learning)

Capital One Capital One · Banking · McLean, VA +3

Applied Researcher I focused on AI Foundations, Recommendation Systems, Personalization, and Reinforcement Learning. The role involves partnering with cross-functional teams to deliver AI-powered products, leveraging technologies like Pytorch and VectorDBs. Responsibilities include building AI foundation models through all development phases, from design to implementation, and conducting high-impact applied research to advance customer experiences. The ideal candidate has a deep understanding of AI methodologies, experience building large deep learning models, and a track record of delivering models at scale.

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. 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.
  3. Build AI foundation models through all phases of development, from design through training, evaluation, validation, and implementation.
  4. Engage in high impact applied research to take the latest AI developments and push them into the next generation of customer experiences.
  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, or M.S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 2 years of experience in Applied Research
  • hands-on experience developing AI foundation models and solutions using open-source tools and cloud computing platforms
  • deep understanding of the foundations of AI methodologies
  • experience building large deep learning models
  • expertise in one or more of the following: training optimization, self-supervised learning, robustness, explainability, RLHF
  • engineering mindset as shown by a track record of delivering models at scale both in terms of training data and inference volumes
  • experience in delivering libraries, platform level code or solution level code to existing products

Nice to have

  • LLM
  • NLP
  • deep learning theory
  • transfer learning
  • model adaptation
  • model guidance
  • tokenization
  • data quality
  • dataset curation
  • labeling

What the JD emphasized

  • track record of delivering models at scale
  • track record of coming up with high quality ideas or improving upon existing ideas in machine learning, demonstrated by accomplishments such as first author publications or projects
  • own and pursue a research agenda, including choosing impactful research problems and autonomously carrying out long-running projects

Other signals

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