Drimva

Crusoe

Principal Product Manager, AI Infrastructure (Storage)

$285K–$335K

San Francisco, CA - US +1 · On-site · Principal · Full-time · Posted Aug 28, 2026

Location
San Francisco, CA - US
Sunnyvale, CA - US
Experience
8+ years; technical product management experience or product-minded infrastructure engineering experience.
Education
Bachelor’s degree in electrical engineering, computer science, or a related field.

About the job

Own Crusoe’s long-term storage architecture and technology strategy to scale its storage platform for demanding AI workloads.

What you'll do

  • Define the 2–4 year roadmap for block, file, object, local NVMe, and archival storage.
  • Set platform performance, durability, and efficiency targets; guide architecture and long-term investments.
  • Manage vendor strategy and partner with engineering and architecture teams on foundational storage decisions.

What they ask for

  • Bachelor’s degree in electrical engineering, computer science, or a related field.
  • 8+ years of technical product management or product-minded infrastructure engineering experience, with architecture-level ownership at Principal or equivalent scope focused on cloud storage or related infrastructure.
  • Subject matter expertise in storage and strong understanding of hyperscale cloud storage architectures, including block, file, and object storage.
  • Experience managing long-term vendor strategy and evaluating in-house software engineering investment for storage hardware and software.

Nice to have

  • Experience with AI/ML infrastructure, including cloud training and inference workloads.
  • Experience with high-performance storage networking such as RDMA or GPUDirect Storage; major cloud provider storage services or cloud-native storage principles; or managing or mentoring product managers.

Pay

Up to $$285,000 - $335,000 + Bonus (base salary range; bonus amount not stated).

Tools

block, file, object, local NVMe, AWS, Azure, GCP, RDMA

AI focus

Build storage infrastructure for AI workloads and larger AI clusters; bonus experience includes cloud training and inference workloads.

Model-written summary · the original posting is authoritative

First seen Oct 10, 2026 · Last verified Nov 5, 2026 (UTC)