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

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

Applied Researcher II at Capital One focused on AI Foundations, LLM Core, and Agentic AI. The role involves partnering with cross-functional teams to deliver AI-powered products, leveraging technologies like Pytorch and VectorDBs. Responsibilities include building AI foundation models through all development phases (design, training, evaluation, validation, implementation) and engaging in applied research to advance customer experiences. The ideal candidate has a deep understanding of AI methodologies, experience building large deep learning models (language, images, events, graphs), expertise in optimization, self-supervised learning, robustness, explainability, or RLHF, and a track record of delivering models at scale. Experience with LLMs, including pre-training and 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, or MS with equivalent experience
  • 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 (e.g., 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

  • LLM
  • NLP
  • pre-training of large language models
  • SSL techniques
  • model pre-training optimization
  • trained 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

  • 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 delivering libraries, platform level code or solution level code to existing products

Other signals

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