Staff AI Engineer – Remote with Scalable AI Systems

Capital One; Lansing (MI); Remote; USD 245,000 - 279,000; Full time

Benefits offered by this job

Health benefits

Competitive benefits

Job summary

Capital One is seeking a Staff AI Engineer to help build responsible and scalable AI systems. You will partner with engineers, researchers, PMs, and others to deploy AI software components including model training, inference, and orchestration. The role emphasizes responsible AI, observability, and enterprise-wide standards.

The ideal candidate loves building systems, stays current with AI research, and can lead large engineering efforts across teams while mentoring others.

Qualifications

• Bachelor's degree in CS/AI/EE/CE + 8+ years AI/ML experience or Master's + 6+ years AI/ML experience.
• 8+ years of Python/Go/Scala/CUDA/Java experience.

Responsibilities

• Partner with cross-functional team to deliver AI-powered products.
• Design, develop, test, deploy, and support AI software components.
• Leverage Open Source and SaaS AI tech like AWS Ultraclusters, Huggingface, PyTorch.
• Invent foundation model optimization techniques for performance, cost and latency.
• Contribute to technical vision and long-term roadmap of AI systems.
• Set technical direction for enterprise AI architecture across teams.
• Own design/integration of model routing, caching, orchestration for multi-model workloads.
• Champion responsible AI principles with transparency and fairness by design.
• Drive internal education and dissemination of best practices through AI guilds.

Skills

Python/Go/Scala/CUDA/Java experience

Education

Bachelor's or Master's in CS/AI/EE/CE

Tools

AWS Ultraclusters

Huggingface

VectorDBs

PyTorch

Job description

Capital One is seeking a Staff AI Engineer to help build responsible and scalable AI systems. You will partner with engineers, researchers, PMs, and others to deploy AI software components including model training, inference, and orchestration. The role emphasizes responsible AI, observability, and enterprise-wide standards.

The ideal candidate loves building systems, stays current with AI research, and can lead large engineering efforts across teams while mentoring others.