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Super Micro Computer

Modular, energy-efficient server hardware solutions

Staff Solution Architect

Pre-Sales Solution EngineeringUpdated on Nov 14
Full-timeZhonghe District, TaiwanNo salary listed

About the Job

Requirements

  • 8–12 years of related experience, including 5+ years in customer-facing solution architecture for compute, storage, or networking at scale.
  • Deep knowledge of CPU/GPU/DPU integration, including PCIe topology, NVLink/NVSwitch, NUMA, RDMA, and high-speed Ethernet/InfiniBand.
  • Hands-on experience with NVIDIA HGX/MGX and GB200/GB300-class platforms, Spectrum-X or InfiniBand fabric design, and CUDA-stack awareness.
  • Working knowledge of high-performance and software-defined storage, including parallel/object SDS, and its integration into GPU-accelerated environments.
  • Ability to translate AI training/inference, Enterprise AI/AIDP, Video AI, and edge workloads into validated rack-scale designs with quantified performance and TCO.
  • Hands-on experience benchmarking AI clusters and profiling performance; designing and executing workload-driven test plans against vendor reference architectures and blueprints; profiling GPU/CPU/interconnect/storage bottlenecks with NCCL/RCCL, MLPerf, nsys/nvprof, PCIe and fabric telemetry; and translating results into platform and BOM decisions.
  • Hands-on experience with Kubernetes, container orchestration, virtualization, and cloud-native network functions sufficient to support 5G core projects end-to-end, including control- and user-plane separation.
  • A BS in Electrical Engineering, Computer Engineering, Mechanical Engineering, Computer Science, or a related field.

Responsibilities

  • Architect rack- and pod-scale heterogeneous-compute platforms across CPU, GPU, and DPU, including host-to-accelerator interconnect design, GPU fabric topology, data-center fabric architecture, NUMA-aware platform balance, RDMA data-path engineering, and workload-driven DPU placement.
  • Engage architecture before configuration lock for massive-scale AI/GPU clusters and drive end-to-end low-latency fabric design with measured targets.
  • Validate and benchmark NVIDIA GPU cluster platforms and future AMD GPU solutions with the Solution Engineering team; own test plans, run workloads, and interpret results against NVIDIA NCP reference architecture and NVIDIA Blueprints, including AIDP and VSS.
  • Integrate software-defined and disaggregated storage, including VAST DASE and VAST Data Engine, into GPU-accelerated clusters, covering KV-cache tiering, zero-copy data paths, and SDS validation as a repeatable cycle.
  • Co-author joint reference architectures with VAST and NVIDIA, including Vast Ebox/C+D Appliance and AIDP/CNode-X; lead strategic-account architecture from discovery through proof of concept, design-win, and deployment.
  • Productize DCBBS into sellable solutions, define reference configurations with regional product management, and equip regional product management teams with tools, reference materials, and architectural knowledge to promote DCBBS in-region.
  • Maintain partnerships with NVIDIA, AMD, Intel, and Marvell; track their roadmaps 12–18 months ahead and apply them in designs.
  • Produce whitepapers, reference architectures, and field enablement; coordinate across product management, business development, sales, field application engineering, engineering, and marketing; mentor across divisions.

Desired Qualifications

  • An MS or MBA is preferred.
  • Exposure to Video AI and sovereign edge, and experience with Tier-1 telecommunications providers, hyperscalers, or neoclouds, are strongly preferred.

About the Company

Super Micro Computer

Modular, energy-efficient server hardware solutions

Super Micro Computer designs and sells high-performance, energy-efficient server hardware and related software and services for data centers, cloud, AI, 5G, and edge computing. Its modular Building Block Solutions let customers assemble configurable servers, storage, motherboards, and chassis from common components to match workloads. The company emphasizes green computing and power efficiency to reduce energy use in large-scale deployments and enable faster procurement. Its goal is to help customers deploy reliable computing infrastructure quickly through direct sales and an extensive partner network of distributors and value-added resellers.

Company size
5,001-10,000
Company stage
IPO
Headquarters
San Jose, CA
Founded
1993

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