Storage Benchmarking Engineer

Role: Storage Benchmarking Engineer

Location: Santa Clara, CA

Duration: Full-Time - On-Site

The Role

The Storage Benchmarking Engineer will design, execute, and analyze performance benchmarks spanning both industry-standard storage benchmarks (fio, vdbench, SPEC SFS 2020, IO500, SPC-1/SPC-2) and the emerging class of AI/ML storage workloads (MLPerf Storage, DLIO, and GPU-driven training and inference data pipelines). As AI has made storage a first-order bottleneck in the GPU data path, this role sits at the intersection of high-performance storage and large-scale AI infrastructure.

This position demands strong end-to-end performance troubleshooting across the entire stack compute (including GPUs), network (including RDMA/InfiniBand and high-speed Ethernet), and storage together with close collaboration across engineering, product management, marketing, and sales. The ideal candidate has hands-on experience with both classic storage benchmarks and AI data-pipeline benchmarking, a track record engaging benchmark standards organizations and communities, and exceptional communication and writing skills.

What You ll Do

• Configure and scale the HPC/AI lab environment so all systems including GPU servers, high-speed fabrics, and storage achieve maximum efficiency and scale across a variety of test harnesses. Build robust automation so labs can be rapidly configured and reconfigured to meet the demands of different benchmarks.
• Design and execute storage performance benchmarks using industry-standard tools and methodologies, including fio, vdbench, SPEC SFS 2020, IO500, and SPC-1/SPC-2 (or similar).
• Design and execute AI/ML storage benchmarks, including MLPerf Storage, DLIO, and representative AI workloads model training and checkpointing, inference and data ingest, RAG/vector-database access patterns, and GPU-driven I/O paths (e.g., GPUDirect Storage, NFS/RDMA). Characterize storage behavior against reference architectures such as NVIDIA DGX/SuperPOD and BasePOD.
• Perform end-to-end performance troubleshooting and debugging across compute, GPU, network, and storage components to pinpoint and resolve bottlenecks and achieve best-in-class results.
• Develop and maintain automated benchmarking workflows using tools like Ansible, Python, or Bash to ensure rapid provisioning and efficient, repeatable, reproducible results.
• Analyze benchmark results, generate detailed reports, and deliver actionable insights to engineering teams for product optimization.
• Collaborate with engineering, product management, marketing, and sales to align benchmarking efforts with product goals and customer needs.
• Engage directly with benchmark standards organizations (e.g., SPEC, SNIA, MLCommons) and communities to influence methodologies, drive submissions, and stay ahead of industry and AI infrastructure trends.
• Deliver high-impact presentations to internal teams, customers, and external stakeholders, translating complex technical data into clear narratives.
• Write technical marketing documents, whitepapers, and performance summaries to support product launches and customer engagement.
• Maintain comprehensive documentation of benchmarking processes, configurations, and results.
• We are primarily an in-office environment, and you will be expected to work from the Santa Clara office in compliance with Everpure s policies, unless you are on PTO, work travel, or other approved leave.

What You ll Bring

• Bachelor s or Master s degree in Computer Science, Electrical Engineering, or a related field (or equivalent experience).
• 5+ years of experience in storage performance benchmarking or a related technical role.
• Proven expertise with storage benchmarking tools, including:
• Fio flexible I/O tester for workload simulation
• vdbench versatile storage benchmarking for enterprise workloads
• SPEC SFS 2020 filesystem and application-level benchmarking
• IO500 and SPC-1/SPC-2 (or similar) HPC and block storage benchmarking
• Experience benchmarking AI/ML storage workloads, such as MLPerf Storage, DLIO, or characterizing storage for GPU-based training and inference pipelines (data ingest, checkpointing, GPUDirect Storage, RDMA-based access). (Strongly preferred)
• Strong end-to-end performance tuning and troubleshooting skills across compute, network, and storage layers and ideally GPU/accelerator data paths.
• Hands-on experience with automation tools (e.g., Python, Ansible, Bash) for test orchestration and data collection.
• Demonstrated track record of interfacing with engineering, product management, marketing, and sales teams.
• Direct engagement with benchmark standards organizations (e.g., SPEC, SNIA, MLCommons) and storage benchmarking communities.
• Exceptional communication skills, with a proven ability to deliver engaging technical briefs and presentations to technical and non-technical audiences.
• Strong writing skills, ideally authoring technical marketing documents, whitepapers, or performance summaries.
• Proficiency in data analysis and visualization tools (e.g., Python, R, Sheets, Excel, Tableau).
• Deep understanding of and hands-on proficiency with enterprise storage technologies (e.g., NVMe, NVMe-oF, SSDs, and scale-up and scale-out block/file/object storage and distributed systems).
• Experience with Everpure Storage products (e.g., FlashArray, FlashBlade) or similar enterprise block, file, and object storage platforms.
• Familiarity with cloud storage (e.g., AWS S3, Azure Blob, Google Cloud Storage) and HPC or AI training environments.
• Knowledge of containerized and orchestrated benchmarking workflows using Docker or Kubernetes.
• Familiarity with high-speed networking and GPU fabrics (e.g., InfiniBand, RoCE, NVLink) as they relate to storage performance. (Preferred)
• Prior contributions to benchmark standards or open-source performance tools.