
10,000+ employees
Founded 1993
🏥 Healthcare
🏭 Manufacturing
🤖 Artificial Intelligence
Healthcare • Manufacturing • Artificial Intelligence
NVIDIA is a leading technology company specializing in accelerated computing and artificial intelligence. NVIDIA pioneers advancements in graphical processing units (GPUs), cloud computing, data centers, and virtual reality, with a focus on gaming, automotive, healthcare, and robotics industries. The company's innovations, such as NVIDIA Omniverse, transform traditional digital processes by enabling high-fidelity simulations and rendering tasks. Their applications span various industries, from autonomous vehicles using NVIDIA DRIVE to healthcare solutions with NVIDIA Clara, and AI-driven analytics and workflows.
🔥 0 minutes ago
🏄 California – Remote
💵 $224k - $431.3k / year
⏰ Full Time
🟠 Senior
🧑💻 Full-stack Engineer
🦅 H1B Visa Sponsor
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10,000+ employees
Founded 1993
🏥 Healthcare
🏭 Manufacturing
🤖 Artificial Intelligence
Healthcare • Manufacturing • Artificial Intelligence
NVIDIA is a leading technology company specializing in accelerated computing and artificial intelligence. NVIDIA pioneers advancements in graphical processing units (GPUs), cloud computing, data centers, and virtual reality, with a focus on gaming, automotive, healthcare, and robotics industries. The company's innovations, such as NVIDIA Omniverse, transform traditional digital processes by enabling high-fidelity simulations and rendering tasks. Their applications span various industries, from autonomous vehicles using NVIDIA DRIVE to healthcare solutions with NVIDIA Clara, and AI-driven analytics and workflows.
• 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
• 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
• Equity • Benefits
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