Lead Performance Architect, AI Storage & Benchmarks

NetApp; San Jose (CA); Ibrido; USD 228.000 - 339.000; Tempo pieno

Descrizione del lavoro

NetApp in California seeks a Performance Architect to lead the ONTAP performance team across hardware and software projects. You will drive performance direction, analyze workloads, and shape AI benchmark strategy, including MLPerf Storage submissions.

You will collaborate with architects and engineers, optimize for Generative and Agentic AI workloads, and work with GPU partners on high-performance data paths such as GPUDirect Storage and RDMA.

Competenze

• In-depth knowledge of technology trends, architecture differentiation, competitive positioning, and workload optimizations.
• Hands-on experience with AI/ML benchmarks and frameworks and an understanding of how AI training and inference stress storage (throughput, metadata, small random reads, checkpoint write bursts)
• Familiarity with GPU server architectures and high-performance data paths: RDMA, NVMe-oF, GPUDirect Storage, parallel file access
• Extensive knowledge and experience in computer operating systems, computer architecture and design, data structures, software design and programming practices; systems programming in C and/or C++
• Strong scripting skills – Perl, Python (primarily within JupyterHub), and Shell
• Knowledge of storage systems and file systems highly desirable
• Experience publishing benchmark results or taking part in industry groups (e.g., MLCommons, SPEC) a plus
• Exceptional presentation and influencing skills
• The ability to make accurate work estimates and develop predictable plans
• Demonstrated willingness to take on additional tasks and responsibilities that positively contribute to realization of team, department, and company goals

Mansioni

• Evaluate and propose key architecture options to drive performance
• Evaluate ONTAP competitiveness and drive differentiation with a performance focus, including against competitors’ published AI benchmark results
• Optimize ONTAP performance for Generative and Agentic AI workloads across the AI data pipeline: data ingest and preparation, training, checkpointing, inference, KV-cache, and RAG/vector search
• Own ONTAP’s AI benchmark program: plan, run, and publish industry benchmark submissions (e.g., MLPerf Storage, SPECstorage AI_IMAGE) that show competitive leadership
• Define AI benchmark methods and workload models (e.g., checkpoint/restore patterns, inference traces) that turn real customer AI pipelines into tests anyone can repeat
• Work with GPU and AI‑ecosystem partners on reference architectures, certifications, and high-performance data paths (GPUDirect Storage, RDMA, NFS over RDMA, pNFS)
• Work closely with engineering and product management to drive the performance agenda
• Evaluate design alternatives for enhanced performance, and prototype opportunities for performance enhancements
• Design, implement, execute, analyze, interpret, socialize and apply storage-oriented performance measurements and their results, including the creation of tools and automation for running AI benchmarks at GPU‑cluster scale
• Turn benchmark results into technical reports, competitive briefs, and conference talks for product marketing and field teams

Conoscenze

AI/ML benchmarks

GPU server architectures

Operating systems & architectures

C/C++ programming

Scripting: Perl, Python, Shell

Benchmark publication experience

Presentation & influencing skills

Work estimates & planning

Formazione

Bachelor/Master/PhD in Electrical Engineering or Computer Science

Strumenti

GPUDirect Storage

RDMA

NFS over RDMA

pNFS

MLPerf Storage

SPECstorage AI_IMAGE

JupyterHub

Descrizione del lavoro

NetApp in California seeks a Performance Architect to lead the ONTAP performance team across hardware and software projects. You will drive performance direction, analyze workloads, and shape AI benchmark strategy, including MLPerf Storage submissions.

You will collaborate with architects and engineers, optimize for Generative and Agentic AI workloads, and work with GPU partners on high-performance data paths such as GPUDirect Storage and RDMA.