Applied Researcher I (ai Foundations, LLM Core and Agentic Ai)

Capital One Capital One · Banking · New York, NY +3

Applied Researcher I role focused on AI Foundations, LLM Core, and Agentic AI at Capital One. 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 advance 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. 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
  • Experience building large deep learning models (language, images, events, or graphs)
  • Expertise in training optimization, self-supervised learning, robustness, explainability, or RLHF
  • Engineering mindset with a track record of delivering models at scale
  • Experience delivering libraries, platform level code or solution level code

Nice to have

  • LLM
  • NLP
  • pre-training of large language models
  • SSL techniques
  • model pre-training optimization
  • training a large language model from scratch (10B + parameters, 500B+ tokens)
  • deep learning theory
  • ACL, NAACL and EMNLP, Neurips, ICML or ICLR publications
  • optimizing training of very large deep learning models
  • Model Sparsification
  • Quantization
  • Training Parallelism/Partitioning Design
  • Gradient Checkpointing
  • Model Compression
  • optimizing training for a 10B+ model
  • deep learning algorithmic and/or optimizer design
  • compiler design
  • guiding LLMs with further tasks (Supervised Finetuning, Instruction-Tuning, Dialogue-Finetuning, Parameter Tuning)
  • transfer learning
  • model adaptation
  • model guidance
  • deploying a fine-tuned large language model

What the JD emphasized

  • 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
  • track record of delivering models at scale both in terms of training data and inference volumes
  • 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.

Other signals

  • AI Foundations
  • LLM Core
  • Agentic AI
  • applied research
  • foundation models