Lead Storage Software Engineer for AI GPU Clusters

NVIDIA; Santa Clara (CA); On-site; USD 224,000 - 431,000; Full time

Benefits offered by this job

Equity

Benefits

Job summary

NVIDIA is seeking a hands‑on Storage Software Engineer to join the DGXC Storage team as an individual contributor and technical lead. You will write and review production code, triage large storage issues, and help define configuration and tuning standards for high‑performance file systems on GPU infrastructure.

You will collaborate with training, inference, and operations teams, stay hands‑on with kernel/ NVMe‑oF/SPDK sources, and contribute to cutting‑edge AI storage solutions for

Qualifications

• BS/MS/PhD in Computer Science, Electrical Engineering, or related field, or equivalent experience; 12+ years in storage software engineering.
• Contributions to open‑source distributed file systems; hands‑on production code review and scale testing.
• Experience diagnosing storage issues in large GPU/HPC clusters, focusing on I/O and metadata performance.
• Strong proficiency in at least one systems language (C, C++, Rust, Go) and Python; comfort with Linux storage and networking stacks.

Responsibilities

• Contribute to open‑source file systems and distributed storage; upstream fixes and engage with upstream communities.
• Lead storage software development: write/review production code, read kernel/NVMe‑oF/SPDK sources to fix bugs.
• Triage and troubleshoot large‑scale storage issues across tens of thousands of GPUs.
• Validate architecture, run scale tests, and ensure durability and performance targets.

Skills

C/C++

Rust

Go

Python

Linux kernel

NVMe‑oF

SPDK

RDMA / RoCE / InfiniBand

File systems

Education

BS/MS/PhD in CS/EE or related field

Tools

SPDK

libfabric

FUSE

Kubernetes

Job description

NVIDIA is seeking a hands‑on Storage Software Engineer to join the DGXC Storage team as an individual contributor and technical lead. You will write and review production code, triage large storage issues, and help define configuration and tuning standards for high‑performance file systems on GPU infrastructure.

You will collaborate with training, inference, and operations teams, stay hands‑on with kernel/ NVMe‑oF/SPDK sources, and contribute to cutting‑edge AI storage solutions for