Senior Storage Software Engineer - DGX Cloud

NVIDIA

US, CA, Santa ClaraFull-timePosted 1 day ago

Get more Software Engineering openings

A short daily email when similar roles appear in Santa Clara. No account needed.

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, vendors, and internal customers.
  • Comfort operating in a 24/7 production environment where storage incidents directly impact GPU availability, with a security-first approach baked into every build.
  • 100% hands-on engineering. You write and review production code, read file system, kernel, NVMe-oF, or SPDK source to chase bugs, and run scale tests or recovery drills yourself rather than delegating.

Ways to stand out from the crowd:

  • Maintainers or sustained contributions to widely used public projects.
  • Experience crafting or operating storage for AI training or inference at very large GPU scale, with measurable gains in GPU utilization or reductions in I/O bottlenecks.
  • Kernel and file system development experience, metadata scalability, data placement, failure recovery, or HSM or equivalent experience.
  • Kubernetes and CSI driver development for storage.
  • Hands-on experience with SPDK, libfabric, or FUSE performance optimization.

NVIDIA is leading the way in groundbreaking developments in Artificial Intelligence, High-Performance Computing, and Visualization. Our invention serves as the visual cortex of modern computers and is at the heart of our products and services. Our work opens up new universes to explore, enables amazing creativity and discovery, and powers what were once science fiction inventions from artificial intelligence to autonomous cars. NVIDIA is seeking exceptional individuals like you to help us drive the next wave of artificial intelligence.


NVIDIA is widely considered one of the world's most desirable employers in technology. We have some of the world's most forward-thinking and passionate people working for us. If you're creative and autonomous, we want to hear from you!

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 224,000 USD - 356,500 USD for Level 5, and 272,000 USD - 431,250 USD for Level 6.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until September 24, 2026.

This posting is for an existing vacancy. 

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

The average job posting receives 250 applications.

Stand out by tailoring your resume to this specific role. Our AI resume builder highlights the skills and experience that matter most to this employer.

Frequently asked questions

Who is hiring for Senior Storage Software Engineer - DGX Cloud at NVIDIA?+
NVIDIA is actively hiring for this Senior Storage Software Engineer - DGX Cloud role. Click "Apply Now" to submit your application directly on NVIDIA's careers page — Careeronaut doesn't charge employers or candidates for referrals.
When was this Senior Storage Software Engineer - DGX Cloud role posted?+
This listing was first posted on 2026-09-20. We pull the latest copy from the source feed daily, and any role that's taken down gets removed from Careeronaut within seven days so you don't waste time on stale listings.
How should I apply to this Senior Storage Software Engineer - DGX Cloud role?+
Start by tailoring your resume to the posting — most applicants send generic CVs and the first filter recruiters use is keyword relevance. Careeronaut's AI does this automatically: paste the job description, get a matched resume in under a minute, and download as PDF or DOCX.
Where can I find more Software Engineering jobs in Santa Clara?+
Browse all open software engineering roles in Santa Clara on the listing pages linked below. You can filter by salary, remote-friendly, and posting date.