Senior AI Foundations Researcher: LLM Finetuning

Capital One

New York (NY)

On-site

USD 239,000 - 272,000

Full time

14 days+

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Job summary

Capital One is seeking an Applied Researcher 4 focused on AI foundations, LLM customization and finetuning. You will collaborate with data scientists, software engineers, ML engineers and product managers to deliver AI-powered features for customers and business teams.

You will work with PyTorch, AWS Ultraclusters, HuggingFace and Lightning to train and refine foundation models, evaluate their performance, and translate research into production-ready solutions that can scale across the bank's

Qualifications

  • PhD or MS with required experience in AI foundations and LLMs.
  • Experience with large language model pre-training, fine-tuning, and deployment.
  • Strong publication record in AI/ML fields is preferred.

Responsibilities

  • Partner with cross-functional team to deliver AI-powered products.
  • Leverage broad stack (Pytorch, AWS Ultraclusters, Huggingface, Lightning) to extract insights.
  • Build foundation models from design to training, evaluation, and implementation.
  • Translate complex research to tangible business goals.
  • Collaborate with senior researchers to publish technical reports or conference submissions.

Skills

Pytorch
AWS
Huggingface
Lightning
Open-source tools

Education

PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields
MS in EE/CS/AI or related field + 2 years of Applied Research experience

Tools

Pytorch
AWS Ultraclusters
Huggingface

Job description

Capital One is seeking an Applied Researcher 4 focused on AI foundations, LLM customization and finetuning. You will collaborate with data scientists, software engineers, ML engineers and product managers to deliver AI-powered features for customers and business teams.

You will work with PyTorch, AWS Ultraclusters, HuggingFace and Lightning to train and refine foundation models, evaluate their performance, and translate research into production-ready solutions that can scale across the bank's

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