This role involves bridging storage infrastructure with chip design workloads, focusing on understanding and improving storage performance for EDA and HPC applications. Responsibilities include evaluating new storage technologies, designing proof-of-concepts, identifying observability gaps, and influencing strategic decisions regarding data placement and storage policies. The position requires at least 8 years of experience with large-scale networked storage systems in Linux environments and proficiency in scripting for automation.
As a member of the HW Infrastructure Storage Strategy team, you will be the bridge between storage infrastructure and the chip design (e.g. NVIDIA GPUs and CPUs) workloads that depend on it. You will focus on going hands-on to understand how storage behavior impacts EDA (Electronic Design Automation) and HPC workload performance in general — and what to do about it. You’ll evaluate next-generation storage technologies, design POCs grounded in real workload profiles, uncover gaps in observability, and drive strategic decisions like data placement during datacenter transitions.
This is a rare role where you’ll work at the intersection of storage systems and chip design workflows at a scale few companies reach — petabytes of active data, millions of files per job, and thousands of concurrent users. You’ll develop NVIDIA’s storage strategy and have a direct, measurable impact on engineering velocity.
What you'll be doing:
- Lead storage technology evaluations — design and execute POCs for platforms such as NetApp, VAST, and others, using real EDA workload profiles to benchmark performance, metadata behavior, and compatibility
- Analyze how storage configurations and architecture choices impact EDA workload runtime in production, and recommend tuning or architectural changes to reduce job execution time
- Identify gaps in storage telemetry and observability — define what metrics are needed and how to correlate storage behavior with jobs running on the environment
- Support datacenter lifecycle programs — expansions, technology refreshes, and site exits — by mapping workload placement, identifying required data, and minimizing data replication scope
- Define storage policies and best practices — data lifecycle, tiering strategies, snapshot/clone workflows (e.g., FlexClone for EDA workspace branching), and capacity planning recommendations
- Collaborate with IT, EDA tool teams, EDA operations, and networking to translate workload requirements into storage architecture guidance
- Investigate storage-related issues affecting production workloads — characterize the impact, identify causes, and recommend preventive measures or design changes
- Contribute to NVIDIA’s storage strategy and roadmap for next-generation compute, AI workloads, and cloud-hybrid infrastructure
What we need to see:
- Bachelor’s degree in Computer Science, Electrical Engineering, or related field (or equivalent experience)
- 8+ years of experience with large-scale NFS and networked storage systems in Linux environments
- Hands-on experience with at least one enterprise or HPC storage platform (NetApp, VAST, Pure, Lustre, GPFS, Ceph, Weka)
- Strong Linux systems skills and storage performance analysis — ability to characterize how storage behavior affects workload outcomes
- Proficient in Python and Bash scripting for automation and tooling
- Experience with cloud-based storage solutions (GCP Filestore, NetApp Cloud Volumes, AWS FSx, or similar)
- Experience with observability and metrics collection systems — building or leveraging dashboards, telemetry pipelines, and alerting to monitor storage health and performance (e.g., Prometheus, Grafana, Netdata, or similar)
Ways to stand out from the crowd:
- Experience with EDA infrastructure or semiconductor design workflows and their unique storage I/O patterns
- Familiarity with enterprise storage features such as instant clones, snapshots, caching tiers, and data tiering policies (e.g., NetApp FlexClone/FlexCache or equivalent)
- Experience correlating infrastructure telemetry with workload performance — e.g., tying storage metrics to job scheduler data (LSF, Slurm)
- Background in datacenter migration planning, capacity modeling, or data placement strategy at multi-petabyte scale
- Experience with container technologies (Docker, Enroot)
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until November 2, 2026.This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.