<div class="content-intro"><p style="text-align: left;"><span style="font-size: 12pt;">SambaNova is a leader in next-generation AI infrastructure, delivering a full-stack inference platform for customers worldwide. At the core of SambaNova's technology is the RDU (Reconfigurable Dataflow Unit) — a chip built on a dataflow architecture rather than the traditional GPU model. Its decode performance is especially strong for agentic workloads like multi-turn agents, code generation, and long-running applications. RDUs are packaged into SambaRack, rack-scale hardware that lets customers deploy state-of-the-art models with better performance, greater energy efficiency, and faster time to value.</span></p></div><h3><strong>About the role</strong></h3> <p class="font-claude-response-body break-words whitespace-normal">SambaNova is accelerating from rack-scale to global-scale AI compute clusters. We're looking for a Network Architect join us on this exciting new chapter and own this critical layer end to end.</p> <h3><strong>Responsibilities</strong></h3> <div data-find-provider-scope=""> <div data-sizer-excess="0" data-rocksteady-sizer=""> <div data-rs-index="210" data-index="210" data-last-message="true"> <div> <div data-test-render-count="1"> <div class="group group/message-row"> <div class="group relative relative pb-[var(--msg-assistant-pb,0.75rem)]" data-is-streaming="false"> <div class="font-claude-response relative leading-[1.65rem] [&_pre>div]:bg-bg-000/50 [&_pre>div]:border-0.5 [&_pre>div]:border-border-400 [&_.ignore-pre-bg>div]:bg-transparent [&_.standard-markdown_:is(p,blockquote,h1,h2,h3,h4,h5,h6)]:pl-2 [&_.standard-markdown_:is(p,blockquote,ul,ol,h1,h2,h3,h4,h5,h6)]:pr-8 [&_.progressive-markdown_:is(p,blockquote,h1,h2,h3,h4,h5,h6)]:pl-2 [&_.progressive-markdown_:is(p,blockquote,ul,ol,h1,h2,h3,h4,h5,h6)]:pr-8"> <div> <div class="grid grid-rows-[auto_auto] min-w-0"> <div class="row-start-2 col-start-1 relative grid grid-rows-[auto_auto] isolate min-w-0"> <div class="row-start-1 col-start-1 relative z-[2] min-w-0"> <div> <div> <div class="standard-markdown grid-cols-1 grid [&_>_*]:min-w-0 gap-3 [&_>_*:last-child]:mb-0 print:block print:[&_>_*_+_*]:mt-3 standard-markdown"> <p class="font-claude-response-body break-words whitespace-normal">In this role, you'll be architecting the network fabrics that power hyperscale AI clusters, from RDU-accelerated servers to global-scale deployments, owning the fabric, interconnect, and spine-leaf design decisions that shape how our compute platforms are built. You'll partner closely with hardware engineering, supply chain, and customer-facing teams to translate workload requirements into platform roadmaps, taking a vendor-agnostic approach that avoids lock-in. Your architecture decisions will directly shape the products we ship and how fast the world's largest AI clusters come online.</p> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> <h3><strong>Required Qualifications</strong></h3> <ul class="[li_&]:mb-0 [li_&]:mt-1 [li_&]:gap-1 [&:not(:last-child)_ul]:pb-1 [&:not(:last-child)_ol]:pb-1 list-disc flex flex-col gap-1 pl-8 mb-3 print:block print:space-y-1"> <li class="font-claude-response-body whitespace-normal break-words pl-2">12+ years designing or architecting infrastructure for hyperscale, AI, HPC, or large-scale data center environments</li> <li class="font-claude-response-body whitespace-normal break-words pl-2">Deep working knowledge of AI cluster networking, including InfiniBand, RoCEv2, Ethernet-based AI fabrics, 400G/800G interconnects, and GPU-to-GPU east-west traffic patterns</li> <li class="font-claude-response-body whitespace-normal break-words pl-2">Experience with lossless Ethernet designs, including QoS, ECN, PFC, congestion management, buffer tuning, and failure-domain isolation</li> <li class="font-claude-response-body whitespace-normal break-words pl-2">Strong understanding of spine-leaf architectures and associated technologies (VXLAN, EVPN, BGP, ECMP, underlay/overlay design, network automation)</li> <li class="font-claude-response-body whitespace-normal break-words pl-2">Familiarity with high-speed optics and cabling for AI clusters, including 400G/800G transceivers, DAC/AOC, fiber topology, and link budgets</li> <li class="font-claude-response-body whitespace-normal break-words pl-2">Experience translating customer and workload requirements into product requirements, platform roadmaps, and architecture tradeoffs</li> <li class="font-claude-response-body whitespace-normal break-words pl-2">Vendor-agnostic approach to platform selection and interoperability across switch, NIC, optical, and fabric ecosystems</li> <li class="font-claude-response-body whitespace-normal break-words pl-2">Ability to partner across hardware engineering, supply chain, operations, and customer-facing teams to bring platforms from concept through deployment</li> </ul> <h3><strong>Preferred Qualifications</strong></h3> <ul class="[li_&]:mb-0 [li_&]:mt-1 [li_&]:gap-1 [&:not(:last-child)_ul]:pb-1 [&:not(:last-child)_ol]:pb-1 list-disc flex flex-col gap-1 pl-8 mb-3 print:block print:space-y-1"> <li class="font-claude-response-body whitespace-normal break-words pl-2">Experience with AI compute platforms: GPU/accelerator systems, rack-scale compute, NVLink/NVSwitch-class fabrics, PCIe/CXL, DPUs/IPUs</li> <li class="font-claude-response-body whitespace-normal break-words pl-2">Understanding of rack-level power, thermal, mechanical, and serviceability requirements, and how network choices drive them (NIC selection, cabling density, PCIe lane allocation)</li> <li class="font-claude-response-body whitespace-normal break-words pl-2">Background in large-scale fleet operations: lifecycle management, reliability, observability, telemetry, field issue resolution</li> </ul><div class="content-pay-transparency"><div class="pay-input"><div class="description"><p>Base Salary Range:</p></div><div class="title">Base Pay Range</div><div class="pay-range"><span>$250,000</span><span class="divider">—</span><span>$330,000 USD</span></div></div></div><div class="content-conclusion"><p><strong><span style="font-family: helvetica, arial, sans-serif; font-size: 12pt;">Submission Guidelines<br></span></strong><span style="font-family: helvetica, arial, sans-serif; font-size: 12pt;">Please note that in order to be considered an applicant for any position at SambaNova Systems, you must submit an application form for each position for which you believe you are qualified. </span></p> <p><strong><span style="font-family: helvetica, arial, sans-serif; font-size: 12pt;">EEO Policy<br></span></strong><span style="font-family: helvetica, arial, sans-serif; font-size: 12pt;">SambaNova Systems is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard basis of age (40 and over), color, disability, gender identity, genetic information, marital status, military or veteran status, national origin/ancestry, race, religion, creed, sex (including pregnancy, childbirth, breastfeeding), sexual orientation, and any other applicable status protected by federal, state, or local laws.</span></p> <p style="text-align: left;"><span style="font-family: helvetica, arial, sans-serif; font-size: 12pt;"><strong>Benefits Summary for US-Based, Full-Time Employment Positions</strong><br>SambaNova offers a competitive total rewards package, including the base salary, plus equity and benefits. We cover 95% premium coverage for employee medical insurance, and 77% premium coverage for dependents and offer a Health Savings Account (HSA) with employer contribution. We also offer Dental, Vision, Short/Long term Disability, Basic Life, Voluntary Life, and AD&D insurance plans in addition to Flexible Spending Account (FSA) options like Health Care, Limited Purpose, and Dependent Care. Our library of well-being benefits available to you and your dependents includes a full subscription to Headspace, Gympass+ membership with access to physical gyms, One Medical membership, counseling services with an Employee Assistance Program, and much more.</span></p></div>