Technical Lead, Ai/ml Storage

Google Google · Big Tech · Seattle, WA +1

Technical Lead for AI/ML Storage at Google, focusing on managing and optimizing storage solutions for AI/ML workloads. This role involves leading product design, developing AI/ML client libraries, and driving storage innovation across Google Cloud Platform services. Requires extensive software development experience, including building ML solutions and infrastructure, with a focus on performance engineering and the evolving AI/ML landscape.

What you'd actually do

  1. Manage AI/ML solutions, bench marking, performance investigation and storage optimizations, and client side products.
  2. Lead product design, develop and enhance AI/ML client libraries for storage.
  3. Work with various teams across GCP, including core GCS, file solutions, GKE, Cloud ML Compute Services (CMCS), and networking to drive storage innovation for AI/ML.
  4. Build knowledge in file systems, operating systems, performance engineering, and the evolving AI/ML landscape.

Skills

Required

  • software development
  • programming language
  • software products testing
  • software design
  • software architecture
  • machine learning solutions
  • AI frameworks
  • AI methods
  • infrastructure development
  • distributed systems
  • networks
  • compute technologies
  • storage
  • hardware architecture

Nice to have

  • Master’s degree or PhD
  • engineering
  • computer science
  • data structures
  • algorithms
  • technical leadership
  • performance engineering
  • bench marking
  • analysis frameworks for the full AI/ML stack
  • storage infrastructure
  • AI/ML frameworks

What the JD emphasized

  • 8 years of experience with software development in a programming language
  • 3 years of experience building or leveraging machine learning solutions and developing applications utilizing AI frameworks and methods
  • 3 years of experience building and developing infrastructure, distributed systems or networks, or experience with compute technologies, storage, or hardware architecture
  • Experience in performance engineering, including bench marking and developing analysis frameworks for the full AI/ML stack

Other signals

  • AI/ML Storage
  • AI/ML client libraries
  • storage innovation for AI/ML
  • AI/ML landscape
  • AI/ML frameworks