10 days ago
Base Salary
$224k - $431k/yr
Responsibilities
- Contribute production code, fixes, and features to open-source parallel and distributed file systems and distributed object storage.
- Write and review production code and inspect kernel, file-system, NFS, NVMe-oF, or SPDK source to diagnose bugs.
- Triage and root-cause storage issues across very large GPU clusters, including I/O performance, metadata performance, data corruption, and recovery problems.
- Validate storage architectures and capabilities through scale tests, benchmarks, recovery drills, and performance and durability qualification.
- Define configuration, tuning, and operational best practices for high-performance file systems on GPU infrastructure.
- Collaborate with training, inference, accelerated-computing, SRE, operations, networking, security, cloud-provider, neocloud, and storage-vendor teams.
- Use AI coding and agentic tools to support development, debugging, validation, and operations.
Requirements
- Bachelor’s, master’s, or PhD in Computer Science, Electrical Engineering, or a related field, or equivalent experience.
- More than 12 years of direct storage software engineering experience, including extensive work with a high-performance parallel or distributed file system at multi-petabyte scale.
- Contributions to open-source distributed or parallel file-system projects and hands-on experience writing and reviewing production code.
- Experience diagnosing storage issues in large GPU or HPC clusters, including I/O and metadata performance analysis.
- Strong proficiency in at least one of C, C++, Rust, or Go, plus proficiency in Python.
- Experience with Linux kernel storage and networking stacks, including the block layer, RDMA, RoCE, InfiniBand, NVMe, page cache, VFS, and multipath.
- Understanding of object storage such as S3 or Swift and block storage such as NVMe-oF and iSCSI.
- Ability to communicate complex technical trade-offs to engineers, SREs, vendors, and internal customers and operate in a 24/7 production environment.
- Preferred experience includes maintaining widely used public projects, AI-training or inference storage, kernel and file-system development, metadata scalability, data placement, failure recovery, HSM, Kubernetes, CSI driver development, SPDK, libfabric, or FUSE performance optimization.
Benefits
- Equity and benefits are provided.
- Base salary is determined by location, experience, and other objective job-related factors.
- The application is expected to remain open at least until November 3, 2026.
- This posting is for an existing vacancy.
About Nvidia
Nvidia designs and sells GPUs and accelerated computing platforms for data centers, AI/ML, graphics, gaming, and automotive, monetizing through hardware, software platforms (CUDA, AI frameworks), and systems like DGX and networking. Customers include cloud providers, enterprises, researchers, and OEMs. Founded in 1993 and headquartered in Santa Clara, it is a public company traded on NASDAQ under NVDA.