Applied Researcher I (ai Foundations, LLM Customization, Finetuning, Reinforcement Learning)

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

Applied Researcher role focused on AI Foundations, LLM Customization, Finetuning, and Reinforcement Learning within a fintech company. The role involves partnering with cross-functional teams to deliver AI-powered products, leveraging technologies like Pytorch and VectorDBs, and building AI foundation models through all development phases. It emphasizes applied research to improve customer experiences and requires 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. 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. 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, with an exception that required degree will be obtained on or before the scheduled start date or M.S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 2 years of experience in Applied Research
  • 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
  • a professional with a 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
  • Possess the ability to own and pursue a research agenda, including choosing impactful research problems and autonomously carrying out long-running projects

Nice to have

  • PhD focus on NLP or Masters with 5 years of industrial NLP research experience
  • Multiple publications on topics related to the pre-training of large language models (e.g. technical reports of pre-trained LLMs, SSL techniques, model pre-training optimization)
  • Member of team that has trained a large language model from scratch (10B + parameters, 500B+ tokens)
  • Publications in deep learning theory
  • Publications at ACL, NAACL and EMNLP, Neurips, ICML or ICLR
  • PhD focused on topics related to guiding LLMs with further tasks (Supervised Finetuning, Instruction-Tuning, Dialogue-Finetuning, Parameter Tuning)
  • Demonstrated knowledge of principles 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

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
  • experience building large deep learning models
  • deep understanding of the foundations of AI methodologies

Other signals

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