Principal Performance Architect - ONTAP

About the role

NetApp is looking for a Principal Performance Architect for ONTAP in San Jose, California. You will lead the ONTAP performance team across hardware and software projects for ONTAP releases, working with architects across engineering development teams to set the performance agenda and guide technical work. The role sits in the Research and Development function, which focuses on technology advances, competitive market and customer workload needs, product cost and time-to-market, and product quality.

Key responsibilities

• Evaluate and propose architecture options to drive performance, and assess ONTAP competitiveness 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 submissions such as MLPerf Storage and SPECstorage AI_IMAGE, and define benchmark methods and workload models.
• Work with GPU and AI ecosystem partners on reference architectures, certifications and high-performance data paths (GPUDirect Storage, RDMA, NFS over RDMA, pNFS).
• Prototype performance enhancements and design, implement and analyze storage-oriented performance measurements, including tools and automation for AI benchmarks at GPU-cluster scale.
• Turn benchmark results into technical reports, competitive briefs and conference talks for product marketing and field teams.

Required profile

• Typically 12-15 years of relevant experience.
• Bachelor of Science, Master of Science or PhD in Electrical Engineering or Computer Science, or equivalent experience.
• Exceptional presentation and influencing skills, and the ability to make accurate work estimates and develop predictable plans.
• Willingness to take on additional tasks that help team, department and company goals.

Required skills

• Systems programming in C and/or C++.
• Scripting in Perl, Python (mainly in JupyterHub) and Shell.
• Familiarity with GPU server architectures and high-performance data paths: RDMA, NVMe-oF, GPUDirect Storage, parallel file access.
• Knowledge of storage systems and file systems highly desirable.
• Experience publishing benchmark results or taking part in industry groups (e.g., MLCommons, SPEC) is a plus.

What we offer

• Target salary range of 227,800 - 338,800 USD, depending on location, qualifications, experience and education.
• Benefits that may include health insurance, life insurance, retirement or pension plans, paid time off, leave options, an employee stock purchase plan and/or restricted stock units.
• A hybrid working environment, with in-office and/or in-person expectations shared during recruitment.