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NVIDIA

Senior Storage Software Engineer – DGX Cloud

NVIDIA

. Contribute code to open-source parallel and distributed file systems and distributed object storage .

Posted 9/21/2026full-timeRemote • California • United StatesSenior💰 $224,000 - $431,250 per yearWebsite

Core Competencies

Role fit
Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates extensive experience in storage software engineering, particularly with high-performance parallel and distributed file systems, and possesses strong proficiency in systems programming languages. Capable of diagnosing complex storage issues in large GPU clusters and validating storage architecture against performance and durability targets.

Highest-signal resume keywords
Storage Software EngineeringHigh-Performance Parallel File SystemsSystems Programming (C, C++, Rust, Go)Linux Kernel Storage and NetworkingKubernetes and CSI Driver Development

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills
Storage Software EngineeringHigh-Performance Parallel File SystemsSystems Programming (C, C++, Rust, Go)PythonLinux Kernel DevelopmentI/O and Metadata Performance AnalysisSPDK Performance OptimizationScale Testing and Recovery DrillsObject Storage (S3, Swift-class)Block Storage (NVMe-oF, iSCSI)
Soft Skills
Strong Written and Verbal CommunicationComfort in 24/7 Production EnvironmentSecurity-First Approach
Tools & Technologies
SPDKLibfabricFUSEKubernetesCSI Driver
Industry Keywords
Open-Source ContributionsDistributed Object StorageGPU ClustersHPC ClustersData Corruption Recovery

Tech Stack

Tools & technologies
CloudKubernetesLinuxNFSPythonRustSwiftC++Go

About the role

Key responsibilities & impact
  • Contribute code to open-source parallel and distributed file systems and distributed object storage
  • Upstream fixes and features and engage with upstream communities and maintainers
  • Write and review production code as a hands-on storage software lead
  • Read kernel, NFS, NVMe-oF, or SPDK source to diagnose bugs
  • Make final technical calls on storage deliveries against measurable targets
  • Triage, troubleshoot, and root-cause complex storage issues across very large GPU clusters
  • Investigate I/O and metadata performance, data corruption, and recovery
  • Validate storage architecture, capabilities, performance, and durability
  • Run scale tests, benchmarks, and recovery drills
  • Qualify new builds against measurable performance and durability targets
  • Define and recommend configuration, tuning, and operational best practices for high-performance file systems on GPU infrastructure
  • Help operators and internal customers apply storage guidelines
  • Collaborate with training, inference, accelerated-computing, SRE, operations, networking, and security teams
  • Collaborate with cloud providers, neocloud operators, and storage vendors on common architecture
  • Use modern AI coding and agentic tools to accelerate building, debugging, validation, and operations

Requirements

What you’ll need
  • 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
  • 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
  • Hands-on experience writing and reviewing production code, examining file system, kernel, NVMe-oF, or SPDK source, and conducting scale tests or recovery drills
  • 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
  • Proficiency in Python
  • Comfortable in Linux kernel storage and networking stacks, including block layer, RDMA / RoCE / InfiniBand, NVMe, page cache, VFS, and multipath
  • Solid understanding of object storage, including S3 / Swift-class
  • Solid understanding of block storage, including NVMe-oF and iSCSI
  • Strong written and verbal communication
  • Comfort operating in a 24/7 production environment
  • Security-first approach
  • Maintainers or sustained contributions to widely used public projects
  • Experience crafting or operating storage for AI training or inference at very large GPU scale
  • 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

Benefits

Comp & perks
  • Equity
  • Benefits 📊 Check your resume score for this job Improve your chances of getting an interview by checking your resume score before you apply. Check Resume Score