Staff AI Engineer - Remote, Foundation AI Systems

Capital One; Cambridge (MA); Remote; USD 269,000 - 307,000; Full time

Job summary

Capital One is seeking a Staff AI Engineer in Cambridge, MA, with remote options. You will design and build production AI systems, including foundation model training, inference, and multi-model workflows, partnering with engineers, researchers, PMs, and security teams.

You will mentor teams, drive architecture standards, and advance responsible AI across the enterprise, helping to deliver scalable, high-performance AI infrastructure and product experiences.

Qualifications

• Bachelor's or Master’s degree in a related field with substantial AI/ML experience.
• 8+ years programming experience in Python, Go, Scala, CUDA, or Java.
• Experience designing and operating scalable AI/ML systems in production.

Responsibilities

• Partner with cross-functional teams to deliver AI-powered products.
• Design, develop, test, deploy, and support AI software components including model training and inference.
• Leverage Open Source and SaaS AI tech such as AWS Ultraclusters, HuggingFace, PyTorch, and VectorDBs.
• Invent and optimize foundation model techniques for performance, cost, and latency.
• Contribute to Capital One's long-term AI systems roadmap and architecture standards.
• Own model routing, caching, and orchestration for hybrid/multi-model workloads.
• Promote responsible AI with transparency, reproducibility, and fairness-by-design.
• Mentor teams and share best practices across architecture councils.

Skills

Python

Go

Scala

CUDA

Java

Education

Bachelor's Degree in CS/AI/EE/CE

Master's Degree in CS/AI/EE/CE

Tools

AWS

Google Cloud

Azure

HuggingFace

PyTorch

VectorDBs

Job description

Capital One is seeking a Staff AI Engineer in Cambridge, MA, with remote options. You will design and build production AI systems, including foundation model training, inference, and multi-model workflows, partnering with engineers, researchers, PMs, and security teams.

You will mentor teams, drive architecture standards, and advance responsible AI across the enterprise, helping to deliver scalable, high-performance AI infrastructure and product experiences.