Network Architect

The era of pervasive AI has arrived. In this era, organizations will use generative AI to unlock hidden value in their data, accelerate processes, reduce costs, drive efficiency and innovation to fundamentally transform their businesses and operations at scale. SambaNova Suite™ is the first full-stack, generative AI platform, from chip to model, optimized for enterprise and government organizations. Powered by the intelligent SN40L chip, the SambaNova Suite is a fully integrated platform, delivered on-premises or in the cloud, combined with state-of-the-art open-source models that can be easily and securely fine-tuned using customer data for greater accuracy. Once adapted with customer data, customers retain model ownership in perpetuity, so they can turn generative AI into one of their most valuable assets. About SambaNova Systems: Join the company that's building the future of AI computing. At SambaNova, we are disrupting the AI and high-performance computing space with our integrated hardware and software platform. Our DataScale systems and SambaFlow software are pushing the boundaries of what's possible with generative AI and large language models. We are a team of passionate innovators tackling some of the world's most challenging computational problems. The Opportunity: We are seeking a visionary Network Architect to define and drive the future of our hyperscale AI compute platform. In this critical role, you will architect the foundational networks powering next-generation AI workloads, from RDU-accelerated servers to global-scale AI clusters. You will sit at the intersection of hardware engineering, product strategy, and large-scale deployment, translating complex technical and customer requirements into scalable, cost-optimized, and high-performance infrastructure solutions. We are looking for someone with: 12+ years of experience designing, architecting, or productizing compute infrastructure for hyperscale, AI, HPC, cloud, or large-scale data center environments Deep experience with AI compute platforms , including GPU/accelerator systems, high-density server architectures, rack-scale compute, and cluster-level design considerations Strong understanding of server and rack-level architecture , including power, thermal, mechanical, serviceability, firmware, and platform integration requirements Experience translating customer, workload, and deployment requirements into compute product requirements , platform roadmaps, technical specifications, and architecture tradeoffs Familiarity with modern AI infrastructure components such as GPU servers, accelerator trays, NVLink/NVSwitch-class fabrics, PCIe/CXL, high-speed NICs, DPUs/IPUs, and storage-attached compute architectures Deep working knowledge of AI cluster networking and fabric dependencies , including InfiniBand, RoCEv2, Ethernet-based AI fabrics, 400G/800G interconnects, and GPU-to-GPU east-west traffic patterns Experience with lossless or near-lossless Ethernet designs , including QoS, priority mapping, ECN, PFC, congestion management, buffer tuning, telemetry, and failure-domain isolation Strong understanding of spine-leaf network architectures and associated technologies such as VXLAN, EVPN, BGP, MLAG, ECMP, underlay/overlay design, multi-tenant segmentation, and network automation Ability to design and evaluate solutions across multiple networking platforms and operating models , with a vendor-agnostic approach to architecture, platform selection, interoperability, and lifecycle strategy Experience comparing and integrating technologies across leading switch, NIC, accelerator, optical, and fabric ecosystems, while avoiding unnecessary vendor lock-in Familiarity with high-speed optics and cabling implications for AI clusters, including 400G/800G DR4, FR4, LR4, SR, DAC/AOC, fiber topology, link budgets, and transceiver interoperability Ability to evaluate how network choices impact compute product requirements, including NIC selection, DPU/IPU integration, PCIe l