Applied AI Researcher II — Foundations & LLMs

Capital One; Cambridge (MA); On-site; USD 263,000 - 300,000; Full time

Job summary

Capital One in Cambridge, MA seeks an Applied Researcher II to advance AI foundations, train large models and deliver production-ready AI systems powering customer experiences. You will collaborate with data scientists, software engineers and product managers, leveraging PyTorch, AWS, HuggingFace and other tools to unlock insights from massive data.

You will push the state of the art through applied research, own a research agenda, publish where possible, and ensure solutions scale in training

Qualifications

• PhD or MS with substantial applied research in AI/ML.
• Experience building large DL models (language, vision, graphs).
• Publications, preferably first-author.
• Ability to own a research agenda and run long-term projects.

Responsibilities

• Partner with cross-functional teams to deliver AI-powered products.
• Build AI foundation models from design to training, evaluation, validation and deployment.
• Engage in applied research to push latest AI developments into customer experiences.
• Translate the complexity of your work into tangible business goals.

Skills

Pytorch

AWS

Huggingface

Lightning

VectorDBs

DL Models

Model Training

Research Agenda

Education

PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics or related fields

MS in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics or related fields

Tools

PyTorch

AWS Ultraclusters

Hugging Face

Job description

Capital One in Cambridge, MA seeks an Applied Researcher II to advance AI foundations, train large models and deliver production-ready AI systems powering customer experiences. You will collaborate with data scientists, software engineers and product managers, leveraging PyTorch, AWS, HuggingFace and other tools to unlock insights from massive data.

You will push the state of the art through applied research, own a research agenda, publish where possible, and ensure solutions scale in training