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

Capital One Capital One · Banking · New York, NY +4

Applied Researcher II 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, AWS, Huggingface, and VectorDBs. Responsibilities include building AI foundation models through all development phases (design, training, evaluation, validation, implementation) and conducting high-impact applied research to improve customer experiences. The ideal candidate has a strong technical background, experience building large deep learning models, expertise in areas like training optimization, self-supervised learning, robustness, explainability, or RLHF, and a track record of delivering models at scale. Experience with LLM pre-training, optimization, or fine-tuning is highly preferred.

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 2 years of experience in Applied Research OR MS in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields 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
  • Experience building large deep learning models (language, images, events, or graphs)
  • Expertise in one or more of: training optimization, self-supervised learning, robustness, explainability, RLHF
  • Engineering mindset
  • 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
  • Ability to own and pursue a research agenda

Nice to have

  • PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering or related fields
  • LLM expertise
  • NLP research experience
  • Publications on pre-training of large language models
  • Experience training large language models (10B+ parameters, 500B+ tokens)
  • Publications in deep learning theory
  • Publications at ACL, NAACL, EMNLP, Neurips, ICML, or ICLR
  • Experience with 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
  • Experience with 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

  • delivering models at scale
  • track record of delivering models at scale
  • track record of coming up with new ideas or improving upon existing ideas in machine learning
  • publications
  • PhD focus on NLP or Masters with 5 years of industrial NLP research experience
  • Member of team that has trained a large language model from scratch (10B + parameters, 500B+ tokens)

Other signals

  • building AI foundation models
  • applied research
  • delivering models at scale
  • delivering libraries, platform level code or solution level code