Senior Software Engineer, Storage Ai/ml

Google Google · Big Tech · Seattle, WA +1

Senior Software Engineer role focused on optimizing Google Cloud Storage for AI/ML workloads. The role involves developing specialized AI client libraries, benchmarking performance, investigating storage optimizations, and collaborating with various GCP teams to drive storage innovation for AI/ML. Requires experience with large-scale infrastructure, distributed systems, and AI/ML infrastructure.

What you'd actually do

  1. Review code developed by other developers and provide feedback to ensure best practices (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).
  2. Senior SWE in the Tessellation team where main charter for AI/ML storage solutions: focusing on AI/ML solutions such as client side distributed caching, benchmarking, performance investigation and storage optimizations.
  3. Lead feature design, develop and tune benchmarks for GCP Storage systems with the goal to make GCP Storage the best for AI/ML workloads.
  4. Work closely with various teams across GCP, including core GCS, File Solutions, GKE, Cloud ML Compute Services (CMCS), and Networking to driving storage innovation for AI/ML.
  5. Build deep technical expertise in file systems, operating systems, performance engineering, and the rapidly evolving AI/ML landscape.

Skills

Required

  • software development in one or more programming languages (Rust, Java, Python)
  • developing large-scale infrastructure, distributed systems or networks, or experience with compute technologies, storage or hardware architecture
  • Storage infrastructure and AI/ML infra (e.g., TensorFlow), artificial intelligence, deep learning, inference frameworks, training frameworks

Nice to have

  • data structures and algorithms
  • technical leadership role
  • developing accessible technologies

What the JD emphasized

  • AI/ML storage solutions
  • AI/ML workloads
  • storage optimizations
  • driving storage innovation for AI/ML

Other signals

  • optimizing Cloud Storage for cutting-edge AI/ML workloads
  • engineering specialized AI client libraries
  • benchmarking, performance investigation and storage optimizations
  • driving storage innovation for AI/ML