Staff AI Engineer: Build Scalable, Responsible AI

Capital One; McLean (VA); On-site; USD 269,000 - 307,000; Full time

Job summary

Capital One is seeking an experienced Staff AI Engineer to help build responsible and scalable AI systems that power real-time banking experiences. You will collaborate with engineers, researchers, PMs, and product teams to design and deploy AI components, including foundation models, multi-model workflows, and governance frameworks.

The role emphasizes responsible AI, observability, and enterprise-wide architecture standards, with opportunities to mentor teams and influence long-term AI

Qualifications

• Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields with at least 8 years of AI/ML experience, or a Master's + 6 years.
• At least 8 years of experience programming with Python, Go, Scala, CUDA, or Java.

Responsibilities

• Partner with cross-functional teams to deliver AI-powered products for associates and customers.
• Design, develop, test, deploy, and support AI software components including foundation models, LLM inference, agents, and observability.
• Leverage Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, and PyTorch.
• Invent foundation model optimization techniques to improve performance, latency, and cost for large-scale AI systems.
• Contribute to the long-term technical roadmap of foundational AI systems at Capital One.
• Set the technical direction for enterprise-wide AI architecture and deployment standards.

Skills

Python

Go

Scala

CUDA

Java

Education

Bachelor's degree + 8+ years

Master's degree + 6+ years

Tools

AWS Ultraclusters

Huggingface

VectorDBs

PyTorch

Job description

Capital One is seeking an experienced Staff AI Engineer to help build responsible and scalable AI systems that power real-time banking experiences. You will collaborate with engineers, researchers, PMs, and product teams to design and deploy AI components, including foundation models, multi-model workflows, and governance frameworks.

The role emphasizes responsible AI, observability, and enterprise-wide architecture standards, with opportunities to mentor teams and influence long-term AI