Software Engineering Manager: AI-Driven Capacity & Scale

Google

Seattle (WA)

On-site

USD 207,000 - 300,000

Full time

13 days ago

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Job summary

Google seeks a Software Engineering Manager to lead capacity planning for Google Cloud Storage (GCS) across multiple teams and sites. You will drive high-throughput capacity strategies, mentor engineers, and influence product direction while managing a large engineering budget and cross-functional relationships.

You will oversee the deployment of scalable storage solutions for internal products like Gmail, Photos, Drive, and YouTube, ensuring reliability and efficiency at planet scale.

Qualifications

  • Bachelor’s degree, or equivalent practical experience.
  • 8 years of experience in software development.
  • 3 years of experience with large-scale infrastructure or distributed systems.
  • 3 years in a technical leadership role.
  • 2 years in people management or team leadership.

Responsibilities

  • Manage staffing, career growth, and high-performance bar for capacity planning.
  • Own strategy and outcomes for capacity planning systems for high-throughput GCS.
  • Guide teams through ambiguity and break down complex supply problems.
  • Coordinate with GCS, Networking, Compute, and Data Center teams on onboarding AI/ML workloads.
  • Drive automation of capacity adjudication and resource optimization.

Skills

Software development
Technical leadership
People management
Capacity planning
Distributed systems
Compute/storage

Education

Bachelor's degree or equivalent
Master's or PhD preferred

Job description

Google seeks a Software Engineering Manager to lead capacity planning for Google Cloud Storage (GCS) across multiple teams and sites. You will drive high-throughput capacity strategies, mentor engineers, and influence product direction while managing a large engineering budget and cross-functional relationships.

You will oversee the deployment of scalable storage solutions for internal products like Gmail, Photos, Drive, and YouTube, ensuring reliability and efficiency at planet scale.

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