Staff Software Engineer, Google Distributed Cloud AI Storage

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.

The Google Distributed Cloud (GDC) Storage team provides the foundational data layer for secure, enterprise-grade file, block, and object storage across edge and on-premises environments. We balance the needs of AI pipelines, which require massively parallel throughput and low latency, with critical enterprise infrastructure built on proven Network Attached Storage (NAS) platforms and standard NFS workflows.

As a Systems/Software Engineer on this team, you will bridge cloud-native Kubernetes orchestration and enterprise scale-out NAS architectures. You will lead technical initiatives to standardize NFS protocol performance, integrate enterprise storage controllers, and automate tenant isolation and declarative volume provisioning across various on-premises deployments.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits

Learn more about benefits at Google . Responsibilities

• Design and implement scalable distributed files (NFS, SMB) and object (S3) storage systems to power demanding AI/ML workloads and critical enterprise applications.
• Lead technical integration of enterprise scale-out NAS and NFSv4 storage architectures into Google’s declarative Kubernetes control plane by building custom CSI drivers, operators, and controllers.
• Engineer high-throughput, ultra-low-latency storage solutions tailored for high-frequency model checkpointing, KV cache offloading, and accelerated database workloads.
• Partner across AI infrastructure, compute, and networking teams to profile performance and optimize data paths using high-speed RoCE and NVMe-oF fabrics.
• Maintain end-to-end operational ownership of core storage services, enforcing strict zero-trust security, robust encryption, and high availability across disconnected edge and air-gapped data centers.

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.

• 8 years of experience programming in C++.

• 5 years of experience testing, and launching software products.

• 5 years of experience building and developing large-scale infrastructure, distributed systems or networks, or experience with compute technologies, storage, or hardware architecture.

• 3 years of experience with software design and architecture.

Preferred qualifications:

• Master’s degree or PhD in Engineering, Computer Science, or a related technical field.

• 8 years of experience with data structures and algorithms.

• 3 years of experience in a technical leadership role leading project teams and setting technical direction.

• 3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.

• Experience building and operating enterprise storage systems (File, Block, Object) and managing containerized workloads with Kubernetes.
• Experience designing and scaling distributed storage, with expertise in NFS protocols (NFSv4/pNFS), file/object systems (S3, scale-out NAS), Kubernetes CSI primitives, and Linux VFS internals.