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X In most instances, this position requires in-person interviews as part of the hiring process.
Minimum qualifications: - Bachelor's degree or equivalent practical experience.
- 5 years of experience designing, building, and operating large-scale distributed systems, with a focus on scalability, fault tolerance, and reliability.
- Experience with storage technology (e.g., block storage, object storage, file storage) and distributed file systems (e.g., Lustre used for AI workloads).
- Experience managing cloud storage solutions for data storage, retrieval, or AI/ML training data.
- Experience designing system architecture for enterprise computing environments supporting AI/ML workloads.
Preferred qualifications: - Experience in designing, building, and operating large-scale distributed systems, with a focus on scalability, fault tolerance, and reliability.
- Experience in compute technologies, storage, or hardware architecture.
- Experience with HPC systems hardware architecture, network interconnect technologies and topologies, and HPC storage file systems.
- Experience in relational Database Management System (RDBMS) kernel or RDBMS storage.
Responsibilities - Design high-performance distributed storage systems optimized for large-scale AI training workloads.
- Optimize data pipelines and storage I/O throughput to eliminate bottlenecks in machine learning training.
- Implement scalable caching strategies to accelerate data access for deep learning frameworks.
- Develop robust system monitoring and telemetry tools to track storage performance and reliability metrics.
- Architect fault-tolerant storage architectures that ensure high availability and data durability.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $174000 - $252000 (USD) 15% bonus target equity benefits
Learn more about benefits at Google .
