Applied Researcher I

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

Applied Researcher I role focused on building AI foundation models through all phases of development, from design through training, evaluation, validation, and implementation. The role involves high-impact applied research to push the latest AI developments into customer experiences, leveraging technologies like Pytorch, AWS, Huggingface, and VectorDBs. Requires a PhD or MS with experience in AI/ML, with expertise in areas like training optimization, self-supervised learning, robustness, explainability, or RLHF, 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 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
  • training a large language model from scratch
  • 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 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
  • publications

Other signals

  • building AI foundation models
  • applied research
  • delivering models at scale