Architect Jobs

About The Position

Requirements

• Typically 12–15 years of relevant experience.
• Bachelor of Science, Master of Science, or PhD in Electrical Engineering or Computer Science, or equivalent experience.
• In-depth knowledge of technology trends, architecture differentiation, competitive positioning, and workload optimizations.
• Hands-on experience with AI/ML benchmarks and frameworks, and understanding of how AI training and inference stress storage through throughput, metadata, small random reads, and checkpoint write bursts.
• Familiarity with GPU server architectures and high-performance data paths, including RDMA, NVMe-oF, GPUDirect Storage, and parallel file access.
• Extensive knowledge and experience in computer operating systems, computer architecture and design, data structures, software design, and programming practices.
• Systems programming experience in C and/or C++.
• Strong scripting skills in Perl, Python (primarily within JupyterHub), and Shell.
• Exceptional presentation and influencing skills.
• Ability to make accurate work estimates and develop predictable plans.
• Willingness to take on additional tasks and responsibilities that contribute positively to team, department, and company goals.

Nice To Haves

• Knowledge of storage systems and file systems.
• Experience publishing benchmark results or participating in industry groups such as MLCommons or SPEC.

Responsibilities

• Evaluate and propose key architecture options to drive performance.
• Assess ONTAP’s performance competitiveness and differentiation, including against competitors’ published AI benchmark results.
• Optimize ONTAP performance for Generative and Agentic AI workloads across data ingest and preparation, training, checkpointing, inference, KV-cache, and RAG/vector search.
• Own ONTAP’s AI benchmark program by planning, running, and publishing industry benchmark submissions such as MLPerf Storage and SPECstorage AI_IMAGE.
• Define AI benchmark methods and workload models, including checkpoint/restore patterns and inference traces, that make real customer AI pipelines repeatable as tests.
• Work with GPU and AI ecosystem partners on reference architectures, certifications, and high-performance data paths, including GPUDirect Storage, RDMA, NFS over RDMA, and pNFS.
• Work closely with engineering and product management to drive the performance agenda.
• Evaluate design alternatives for improved performance and prototype performance-enhancement opportunities.
• Design, implement, execute, analyze, interpret, socialize, and apply storage-oriented performance measurements and results.
• Create 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.

Benefits

• Health insurance
• Life insurance
• Retirement or pension plans
• Paid time off
• Various leave options
• Employee stock purchase plan
• Restricted stock units (RSUs)