Applied AI Researcher - Foundations & LLMs

Capital One National Association; California (MO); On-site; USD 238,600 - 272,300; Full time

Job summary

Capital One is hiring for an Applied AI/ML role focused on building AI foundation models that power customer experiences. You will collaborate with data scientists, software engineers and product managers to turn research into scalable AI products.

The role emphasizes on: developing models, evaluating them rigorously, and translating results into business impact, with a strong emphasis on responsible and robust AI practices in a production environment.

Qualifications

• PhD in Electrical/Computer Engineering, CS, AI, Mathematics or related field by start date (or MS + 2 years experience).
• Strong publication record or proven research execution in ML.
• Experience with large language models (LLMs) and foundational AI methods.

Responsibilities

• Partner with data scientists, software engineers, ML engineers and PMs to deliver AI-powered products.
• Use PyTorch, AWS, HuggingFace and related tools to build insights from large data.
• Develop AI foundation models from design through training, evaluation and deployment.
• Translate complex research into tangible business outcomes with clear metrics.
• Contribute to production-grade libraries and platform-level code for AI capabilities.

Skills

Cross-functional collaboration

Open-source languages

Communication of complex tech

Research agenda ownership

ML model development

Problem solving

Education

PhD in EE/CS/AI or related

MS in EE/CS/AI + 2 years Applied Research

Tools

Pytorch

AWS Ultraclusters

Huggingface

Lightning

VectorDBs

Job description

Capital One is hiring for an Applied AI/ML role focused on building AI foundation models that power customer experiences. You will collaborate with data scientists, software engineers and product managers to turn research into scalable AI products.

The role emphasizes on: developing models, evaluating them rigorously, and translating results into business impact, with a strong emphasis on responsible and robust AI practices in a production environment.