Applied Researcher II

Capital One · Banking · San Jose, CA +2

Applied Researcher II role at Capital One focused on building AI foundation models from design through training, evaluation, validation, and implementation. The role involves applied research to create next-generation customer experiences and delivering models at scale. Requires a strong technical background in deep learning, model optimization, and experience with open-source tools and cloud platforms.

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, with an exception that required degree will be obtained on or before the scheduled start date plus 2 years of experience in Applied Research or M.S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 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, whether on language, images, events, or graphs
  • 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 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

Nice to have

  • LLM
  • 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 optimizing training of very large deep learning models
  • Multiple years of experience and/or publications on one of the following topics: 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

What the JD emphasized

  • track record of delivering models at scale
  • 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
  • publications in deep learning theory
  • publications at ACL, NAACL and EMNLP, Neurips, ICML or ICLR
  • experience building large deep learning models
  • experience in delivering libraries, platform level code or solution level code to existing products

Other signals

  • Applied research
  • Build AI foundation models
  • Deliver models at scale