Staff AI Engineer - Remote Foundations & ML Systems

Capital One

Carson City (NV)

Remote

USD 245,000 - 279,000

Full time

37 hours ago
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Job summary

Capital One is seeking a Staff AI Engineer (Remote Eligible) to help build scalable AI systems and responsible, high‑performing ML platforms. You will design, optimize, and deploy foundational AI components across multi‑model workflows.

You will lead cross‑functional collaboration with engineers, researchers, and product managers, while advancing governance, observability, and model evaluation practices at scale.

Qualifications

  • Bachelor's or Master’s degree in CS/AI/EE/CE or related field with required years of experience
  • 8+ years of experience building AI/ML software or 6+ years with a Master’s degree

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 technologies (AWS Ultraclusters, Huggingface, VectorDBs, PyTorch)
  • Invent model optimization techniques to improve performance, cost, latency, throughput
  • Contribute to technical vision and long-term roadmap of foundational AI systems
  • Set technical direction for enterprise‑wide AI architecture and tooling
  • Own design/integration of model routing, caching, orchestration for hybrid workloads
  • Champion responsible AI principles across system design
  • Drive internal education and dissemination of best practices

Skills

Python
AI systems
Distributed systems

Education

Bachelor's degree
Master's degree

Tools

AWS
Huggingface
VectorDBs
PyTorch

Job description

Capital One is seeking a Staff AI Engineer (Remote Eligible) to help build scalable AI systems and responsible, high‑performing ML platforms. You will design, optimize, and deploy foundational AI components across multi‑model workflows.

You will lead cross‑functional collaboration with engineers, researchers, and product managers, while advancing governance, observability, and model evaluation practices at scale.

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