Applied AI Researcher II - Foundation Models

Capital One National Association; New York, Northern (NY, KY); Hybrid; USD 286,000 - 327,000; Full time

Job summary

Capital One is seeking an Applied Researcher II to join the AI Foundations team. You will partner with data scientists, software engineers, ML engineers, and product managers to deliver AI-powered products that transform how customers interact with their money.

You will leverage PyTorch, AWS Ultraclusters, HuggingFace, and other tools to train and evaluate large models, and build foundation models end-to-end from design to deployment.

Qualifications

• PhD or MS with required experience in applied research.
• Expertise in large-scale DL models, training optimization, self-supervised learning, robustness, explainability, RLHF.
• Publication record or equivalent contributions in ML research.
• Ability to own and pursue a research agenda and deliver long-running projects.

Responsibilities

• Partner with a cross-functional team of data scientists, software engineers, ML engineers and product managers to deliver AI-powered products.
• Leverage a broad stack of technologies - PyTorch, AWS Ultraclusters, Huggingface, Lightning, VectorDBs - to reveal insights from large data.
• Build AI foundation models through all phases of development: design, training, evaluation, validation, implementation.
• Engage in high-impact applied research to push AI developments into customer experiences.
• Translate the complexity of work into tangible business goals.

Skills

Graph Neural Networks

Geometric deep learning

Production systems

Open source involvement

Education

PhD in CS/EE/AI

MS + 4 years applied research

Tools

PyTorch

AWS Ultraclusters

HuggingFace

Lightning

VectorDBs

Job description

Capital One is seeking an Applied Researcher II to join the AI Foundations team. You will partner with data scientists, software engineers, ML engineers, and product managers to deliver AI-powered products that transform how customers interact with their money.

You will leverage PyTorch, AWS Ultraclusters, HuggingFace, and other tools to train and evaluate large models, and build foundation models end-to-end from design to deployment.