Applied Researcher II

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

This role is for an Applied Researcher II at Capital One focused on building AI foundation models and applying state-of-the-art AI to customer-facing products. The role involves research, development, training, evaluation, and implementation of AI models, with a strong emphasis on pushing AI capabilities into next-generation customer experiences. The candidate will work with cross-functional teams and leverage various technologies including Pytorch, AWS, Huggingface, and VectorDBs. Experience in training optimization, self-supervised learning, robustness, explainability, RLHF, and delivering models at scale is required. A PhD or MS in a related field with significant research experience is preferred, along with a publication record.

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 MS with 4 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 (language, images, events, or graphs)
  • Expertise in one or more of the following: training optimization, self-supervised learning, robustness, explainability, RLHF
  • Engineering mindset with a 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 (demonstrated by accomplishments such as 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

  • 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
  • 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 optimizing training of very large deep learning models
  • Multiple years of experience and/or publications on model sparsification, quantization, training parallelism/partitioning design, gradient checkpointing, model compression
  • Experience optimizing training for a 10B+ model
  • Deep knowledge of deep learning algorithmic and/or optimizer design
  • Experience with compiler design
  • 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
  • LLM

What the JD emphasized

  • publication track record
  • first author publications
  • trained a large language model from scratch
  • experience delivering libraries, platform level code or solution level code to existing products
  • experience building large deep learning models
  • experience in delivering models at scale both in terms of training data and inference volumes

Other signals

  • partnering with Academia
  • building production systems
  • apply the state of the art in AI to our business
  • deliver AI-powered products
  • Build AI foundation models through all phases of development
  • high impact applied research
  • take the latest AI developments and push them into the next generation of customer experiences
  • developing AI foundation models and solutions
  • delivering models at scale
  • delivering libraries, platform level code or solution level code
  • track record of coming up with new ideas or improving upon existing ideas in machine learning
  • first author publications
  • own and pursue a research agenda
  • choosing impactful research problems
  • autonomously carrying out long-running projects
  • pre-training of large language models
  • trained a large language model from scratch
  • optimizing training of very large deep learning models
  • guiding LLMs with further tasks