Applied Researcher I

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

Applied Researcher I role focused on building AI foundation models and delivering AI-powered products, leveraging state-of-the-art AI developments for customer experiences. The role involves research, training, evaluation, and implementation of large deep learning models, with a focus on optimization, self-supervised learning, robustness, explainability, and RLHF.

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
  • 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 training optimization, self-supervised learning, robustness, explainability, or RLHF
  • Engineering mindset with a track record of delivering models at scale
  • Experience in delivering libraries, platform level code or solution level code to existing products
  • Ability to own and pursue a research agenda

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)
  • Model Sparsification
  • Quantization
  • Training Parallelism/Partitioning Design
  • Gradient Checkpointing
  • Model Compression
  • Deep knowledge of deep learning algorithmic and/or optimizer design
  • Compiler design
  • Guiding LLMs with further tasks (Supervised Finetuning, Instruction-Tuning, Dialogue-Finetuning, Parameter Tuning)
  • Principles of transfer learning, model adaptation and 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 high quality 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 optimizing training for a 10B+ model

Other signals

  • Applied research
  • Build AI foundation models
  • Deliver AI-powered products