Sciforium

Sciforium

GPU Cluster Engineer, Networking

San Francisco, CA · Senior

Sponsorship not specified$100k-$800kDetected 78 days ago
PythonFull-Stack DevelopmentGitAWSGCPAzureKubernetesAnsiblePrometheusGrafanaCybersecurityNetwork SecurityBudgetingProcurementFirewallVPNCiscoBGP/OSPFNetwork EngineeringResearch

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odds of building a lasting career here

59Sponsors, lottery-bound
Cap-exempt (no lottery)0
Sponsors this role80
Entry-level history0
PERM / green-card track0
Lottery odds (Level IV)94
Fits your clock70

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About the role

  • We are looking for a Senior Network Engineer to own the complete networking stack of our GPU clusters - from the RDMA compute fabric to the data center perimeter to cross-site and cloud connectivity.
  • You will design the network architecture for new large-scale clusters, lead its bring-up, and operate it in production.
  • This role owns everything from the NIC port outward: backend InfiniBand/RoCE fabrics, frontend and storage networks, out-of-band management, firewalls and edge security, and the links that connect our clusters to each other and to the cloud.

Responsibilities

  • Fabric Architecture & Cluster Network Design
  • Greenfield Design: Architect the full network for new GPU clusters - compute/backend fabric, storage network, in-band frontend, out-of-band management (BMC/IPMI), and perimeter - including topology (fat-tree/Clos, rail-optimized), oversubscription analysis, and growth planning.
  • RDMA Fabric Selection: Design and justify InfiniBand (NDR/XDR, Quantum-class) vs. lossless Ethernet/RoCE v2 (Spectrum-X, Tomahawk-class) fabrics for training and inference workloads, including scale-up domains (NVLink/NVSwitch, NVL72-class rack systems).
  • Logical Design: Own IP addressing plans, VLAN/VRF segmentation, and BGP/EVPN-VXLAN underlay-overlay architecture.
  • Lossless Ethernet: Configure and validate RoCE v2 at scale - PFC, ECN/DCQCN congestion control, QoS/DSCP classification, buffer tuning - and prove no-drop behavior under full training load.
  • Routing & Switching: Configure and maintain BGP, OSPF, ECMP, and EVPN-VXLAN across multi-vendor NOSes (Arista EOS, NVIDIA Cumulus, Cisco NX-OS, Junos, SONiC).

Requirements

  • Deep RDMA expertise: lossless RoCE v2 tuning (PFC/ECN/DCQCN) and/or InfiniBand fabric management (subnet managers, adaptive routing, SHARP).
  • Hands-on experience with 100-800G optics, high-radix switching, and Clos/rail-optimized topologies.
  • Experience supporting distributed training at 1,000+ GPU scale, or multi-cluster/cross-site training.
  • Must-Haves:
  • 7+ years designing and operating production data center networks, including at least one large-scale HPC/AI cluster fabric (InfiniBand or RoCE v2) you designed or ran.
  • Expert-level routing and switching: BGP (including EVPN-VXLAN), OSPF, ECMP, VRF segmentation, across at least two major vendor platforms.
  • Production network security experience: enterprise firewalls, site-to-site and remote-access VPNs, segmentation design.
  • Cloud networking experience: VPC design and dedicated interconnects (Direct Connect/ExpressRoute class).
  • Nice-to-Haves:
  • Dark fiber / DWDM procurement and operations experience.
  • BlueField DPU or SmartNIC deployments.
  • Kubernetes networking for GPU serving (CNI, SR-IOV, Multus).

Nice to have

  • Working knowledge of the host-side RDMA stack (MOFED/DOCA, NIC tuning, GPUDirect) and how NCCL/RCCL collectives map onto physical fabric.
  • Network automation proficiency: Python plus Ansible (or Nornir/NAPALM), with Git-based configuration workflows.

Compensation

  • Competitive salary and equity

Benefits

  • Medical, dental, and vision insurance
  • Flexible time off
  • Competitive salary and equity

Equal opportunity

  • Sciforium is an equal opportunity employer.
  • All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.

Visa & Work Authorization

  • Backed by multi-million-dollar funding and direct sponsorship from AMD with hands-on support from AMD engineers the team is scaling rapidly to build the full stack powering frontier AI models and real-time applications.
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