Senior Software Engineer, AI Storage Infrastructure

Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.

US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google.

• 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.

• 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.

• 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.