Senior Software Engineer, AI Storage Infrastructure
About The Position
Google is hiring a software engineer to develop technologies and systems that support products operating at massive scale. The role works on a project critical to Google and may offer opportunities to move between teams and projects as the business evolves. Engineers are expected to be versatile, demonstrate leadership qualities, and take on problems across the full stack. This position focuses on AI storage infrastructure: designing and operating high-performance, reliable distributed storage systems for large-scale AI training workloads. It includes optimizing data pipelines and storage I/O, accelerating data access with caching, monitoring storage performance and reliability, and designing fault-tolerant architectures that provide high availability and data durability.
Requirements
- 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 such as block storage, object storage, or file storage, and distributed file systems such as 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.
Nice To Haves
- Experience 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 system monitoring and telemetry tools to track storage performance and reliability metrics.
- Architect fault-tolerant storage architectures that ensure high availability and data durability.
Benefits
- 15% bonus target
- Equity
- Benefits
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What This Job Offers
Job Type
Full-time
Career Level
Senior
