3 days ago
Base Salary
$174k - $252k/yr
Responsibilities
- Design high-performance distributed storage systems for large-scale AI training workloads.
- Optimize data pipelines and storage I/O throughput to remove machine learning training bottlenecks.
- Implement scalable caching strategies to accelerate data access for deep learning frameworks.
- Develop monitoring and telemetry tools for storage performance and reliability metrics.
- Architect fault-tolerant storage systems that provide high availability and data durability.
Requirements
- Bachelor's degree or equivalent practical experience.
- At least five years of experience designing, building, and operating large-scale distributed systems focused on scalability, fault tolerance, and reliability.
- Experience with block, object, or file storage and distributed file systems such as Lustre for AI workloads.
- Experience managing cloud storage solutions for data storage, retrieval, or AI/ML training data.
- Experience designing system architectures for enterprise computing environments supporting AI/ML workloads.
- Preferred experience with compute technologies, storage, hardware architecture, HPC hardware architecture, network interconnect technologies and topologies, HPC storage file systems, or RDBMS kernels or storage.
Benefits
- Bonus target, equity, and Google benefits are included.
- The posting provides a US compensation range and identifies the role as based in the US.
Categories
About Google
Google builds consumer and enterprise software and services including Search, Android, YouTube, Chrome, Maps, Gmail, and Google Cloud. Its business model centers on digital advertising and paid cloud, software, and hardware offerings (e.g., Pixel and Nest) for consumers, developers, and organizations. Founded in 1998 and headquartered in Mountain View, California, Google operates globally as a subsidiary of Alphabet Inc.