Storage Software Lead for AI GPU Clusters

NVIDIA Corporation; Santa Clara (CA); Hybrid; USD 224,000 - 431,000; Full time

Benefits offered by this job

Equity

Benefits

Job summary

NVIDIA Corporation in Santa Clara is seeking a hands-on Storage Software Engineer to lead and contribute to open-source file systems, ensuring fast, reliable, and durable storage for massive GPU clusters.

You will write and review production code, diagnose complex storage issues, and run scale tests and recovery drills across multi-petabyte scale systems, while collaborating with cross-functional teams and maintaining a security-first mindset.

Qualifications

• 12+ years of direct experience in storage software engineering, including high-performance parallel or distributed file systems.
• Contributions to open-source projects involving distributed or parallel file systems.
• Hands-on engineering: writing and reviewing production code, examining file system/kernel sources, and conducting scale tests or recovery drills.

Responsibilities

• Contribute to open-source file systems and upstream fixes and features.
• Lead storage software efforts, write and review production code, and diagnose bugs in large GPU clusters.
• Triage, troubleshoot, and root-cause large-scale storage issues across tens of thousands of GPUs.
• Validate storage architecture, performance, and durability; run scale tests and recovery drills.
• Define configuration, tuning, and best practices for high-performance file systems on GPU infrastructure.
• Collaborate with training, inference, site reliability, networking, security, cloud providers, and storage vendors.
• Work AI-first using modern AI coding tools to accelerate development and operations.

Skills

C/C++

Rust/Go

Python

Kernel storage

Education

BS/MS/PhD in CS/EE

Tools

SPDK

libfabric

FUSE

Job description

NVIDIA Corporation in Santa Clara is seeking a hands-on Storage Software Engineer to lead and contribute to open-source file systems, ensuring fast, reliable, and durable storage for massive GPU clusters.

You will write and review production code, diagnose complex storage issues, and run scale tests and recovery drills across multi-petabyte scale systems, while collaborating with cross-functional teams and maintaining a security-first mindset.