Applied Researcher II

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

Applied Researcher II 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 significant 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 MS with 4 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 in delivering libraries, platform level code or solution level code
  • 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
  • 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)
  • deep learning theory publications
  • 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 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
  • publications

Other signals

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