Senior Storage Software Engineer - DGX Cloud

NVIDIA DGXC Storage team handles some of the fastest training and inference tasks. Every GPU cycle depends on a storage platform built to keep tens of thousands of accelerators continuously busy. It maintains exabytes of data securely and powers the largest AI workloads worldwide across cloud, neocloud, and on-prem setups. With the growth of accelerated computing, storage is essential. It can make the difference between effective GPU use and wasted potential, and between launching a frontier model on time or missing the deadline by months. We’re looking for a hands-on Storage Software Engineer to join the storage team as an individual contributor and technical lead. You will contribute to open-source parallel and distributed file systems and keep our largest GPU clusters fast, reliable, and durable. You will stay deeply hands-on: writing and reviewing production code, chasing root causes in the field, and setting the configuration and tuning standards our GPU fleets run on. This is a chance to do foundational storage engineering for the AI era at the company that introduced accelerated computing.

What you’ll be doing:

• Contribute to open-source file systems. Contribute code to open-source parallel and distributed file systems, and distributed object storage. Upstream fixes and features, and engage directly with the upstream communities and maintainers.

• Serve as a hands-on storage software lead. Write and review production code yourself, and read kernel, NFS, NVMe-oF, or SPDK source when a bug requires it. Make the final technical calls on storage deliveries against measurable targets.

• Triage and troubleshoot at scale. Triage, troubleshoot, and root-cause large, complex storage issues across very large GPU clusters (tens of thousands of GPUs) — I/O and metadata performance, data corruption, and recovery.

• Validate architecture and capabilities. Validate storage architecture, capabilities, performance, and durability. Run scale tests, benchmarks, and recovery drills, and qualify new builds against measurable performance and durability targets.

• Recommend configuration, tuning, and guidelines. Define and recommend configuration, tuning, and operational best practices for high-performance file systems on GPU infrastructure, and help operators and internal customers apply them.

• Partner broadly. Work with training, inference, and accelerated-computing teams, site-reliability and operations, networking, and security, and collaborate with cloud providers, neocloud operators, and storage vendors on a common architecture.

• Work AI-first. Use modern AI coding and agentic tools day-to-day to accelerate building, debugging, validation, and operations.

What we need to see:

• BS, MS, or PhD in Computer Science, Electrical Engineering, or a related field — or equivalent experience. Over 12 years of direct experience in storage software engineering, including extensive involvement with a high-performance parallel or distributed file system handling multi-petabyte scale.

• Contributions to open-source projects involving a distributed or parallel file system. You are fully engaged in engineering tasks. You write and review production code, examine file system, kernel, NVMe-oF, or SPDK source to identify bugs, and personally conduct scale tests or recovery drills instead of assigning them to others.

• Experience diagnosing and resolving storage problems in extensive GPU or HPC clusters, including analysis of I/O and metadata performance.

• Strong proficiency in at least one systems language (C, C++, Rust, or Go) and proficiency in Python; comfortable in the Linux kernel storage and networking stacks (block layer, RDMA / RoCE / InfiniBand, NVMe, page cache, VFS, multipath).

• Solid understanding of object storage (S3 / Swift-class) and block storage (NVMe-oF, iSCSI).

• Strong written and verbal communication; capable of clarifying complex technical trade-offs to engineers, SREs, vend...