Distinguished Engineer, Storage - AI Cloud

Nvidia

Confirmed live 2 days ago High trust

Quick summary

Work type: On-site
Location: Santa Clara, CA
Salary: $320,000–$488,750 / yr
Posted: 9 days ago
Freshness: Confirmed live 2 days ago
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $208k

This role $404k

$112k most similar roles pay here $529k

This role pays more than 99% of similar roles. Most pay $160,312–$254,925 — the shaded band above. At the midpoint, this role pays about $404k versus about $208k for comparable roles.

Based on 240 similar postings.

Employer

About Nvidia

Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing

Nvidia currently has 896 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 876 roles with salary data.

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At a glance

TL;DR · Distinguished Engineer, Storage - AI Cloud

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As a Distinguished Engineer, Storage – AI Cloud, you will join the AI Cloud Data Storage team to lead the multi-year technical strategy for storage expansion across the Neocloud Provider and Cloud Service Provider ecosystems. You will serve as the chief architect, designing high-performance parallel file systems, object stores, and block storage at exabyte scale to support massive AI training and inference workloads. Your daily responsibilities include developing prototype implementations, defining production-ready durability and availability SLOs, and managing open-source strategies for the storage ecosystem. You will utilize C, C++, Rust, or Go, along with Python, while navigating Linux kernel storage and networking stacks like RDMA and NVMe. This role solves critical bottlenecks in GPU utilization by ensuring high-performance data movement across large-scale infrastructure, ultimately enabling the successful deployment of frontier models within complex cloud environments.

Skills

Lustre GPFS Spectrum Scale WEKA VAST BeeGFS DAOS S3 NVMe-oF NVMesh iSCSI C C++ Rust Go Python Linux Kernel RDMA RoCE InfiniBand SPDK NFS Ceph MinIO RocksDB GitHub

What you'll do

Lead the multi-year technical roadmap and reference architecture for high-performance file, object, and block storage at exabyte scale.
Define "production-ready" standards for cloud providers, including strict durability and availability SLOs measured in 9s.
Perform hands-on engineering tasks including writing production code, investigating root causes of complex issues, and developing prototype implementations.
Establish and lead the open-source strategy to expand the AI storage ecosystem through formal APIs, SDKs, and community engagement.
Integrate AI coding tools into daily workflows to accelerate development, debugging, and infrastructure management across the organization.
Design storage architectures for next-generation GPU workloads, including disaggregated inference, KV caching, and large-scale data versioning.
Mentor senior engineers and represent NVIDIA in industry forums, standards bodies, and technical publications.
Automate all infrastructure management tasks such as live software upgrades, capacity rebalancing, and cross-datacenter data movement.

What we're looking for

BS, MS, or PhD in Computer Science, Electrical Engineering, or a related field, or equivalent experience.
Minimum of 18 years of practical engineering experience in storage technology.
Extensive experience with high-performance parallel file systems like Lustre, GPFS, WEKA, VAST, BeeGFS, or DAOS at multi-petabyte scale.
Expertise in object storage (S3/Swift) and block storage (NVMe-oF, NVMesh, iSCSI).
Proven track record of managing storage platforms at exabyte scale for AI training, HPC, or hyperscale data lakes.
Proficiency in at least one systems language (C, C++, Rust, or Go) and proficiency in Python.
Experience with Linux kernel storage and networking stacks including RDMA, RoCE, InfiniBand, NVMe, and VFS.
Demonstrated ability to use advanced AI coding and autonomous tools to accelerate development and operations.

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