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Sciforium

Serverless AI inference platform

GPU Cluster Engineer - Networking

Network Engineering & AdministrationConfirmed live in the last 24 hours
Full-timeSan Francisco, CA, USAIn-person

About the Job

Requirements

  • At least 7 years designing and operating production data center networks, including experience designing or operating at least one large-scale HPC/AI cluster fabric using InfiniBand or RoCE v2.
  • Expert routing and switching skills, including BGP with EVPN-VXLAN, OSPF, ECMP, and VRF segmentation across at least two major vendor platforms.
  • Deep RDMA expertise in lossless RoCE v2 tuning with PFC, ECN, and DCQCN, and/or InfiniBand fabric management, including subnet managers, adaptive routing, and SHARP.
  • Hands-on experience with 100–800G optics, high-radix switching, and Clos or rail-optimized topologies.
  • Production network security experience with enterprise firewalls, site-to-site and remote-access VPNs, and segmentation design.
  • Proficiency in network automation using Python and Ansible or Nornir/NAPALM, with Git-based configuration workflows.
  • Working knowledge of the host-side RDMA stack, including MOFED/DOCA, NIC tuning, and GPUDirect, and understanding how NCCL/RCCL collectives map onto physical fabric.
  • Cloud networking experience with VPC design and dedicated interconnects such as Direct Connect or ExpressRoute.

Responsibilities

  • Architect the complete network for new GPU clusters, covering compute/backend fabric, storage network, in-band frontend, out-of-band management, and perimeter, including topology, oversubscription analysis, and growth planning.
  • Design and justify InfiniBand versus lossless Ethernet/RoCE v2 fabrics for training and inference workloads, including scale-up domains such as NVLink/NVSwitch and NVL72-class rack systems.
  • Specify switches, NICs, optics, and cabling plans, and produce port maps and cable schedules for Hardware Operations to execute.
  • Own IP addressing plans, VLAN/VRF segmentation, and BGP/EVPN-VXLAN underlay-overlay architecture.
  • Configure and validate RoCE v2 at scale, including PFC, ECN/DCQCN congestion control, QoS/DSCP classification, and buffer tuning; verify no-drop behavior under full training load.
  • Operate InfiniBand systems, including subnet managers, adaptive routing, SHARP in-network aggregation, partition keys, and virtual-lane QoS.
  • Benchmark and sign off new fabrics using perftest/ib_write_bw and nccl-tests/rccl-tests; verify GPUDirect RDMA paths and topology-aware placement with the Systems & Platform team.
  • Root-cause fabric-level issues, including congestion trees, ECMP polarization, link flaps, degraded optics, and network-caused straggler nodes.
  • Configure and maintain BGP, OSPF, ECMP, and EVPN-VXLAN across multi-vendor network operating systems.
  • Own firewalls, VPNs, NAT, ACLs, and network segmentation between production, research, and management domains.
  • Generate network configurations from a source of truth such as NetBox and deploy them using Ansible/Nornir/NAPALM with Git-reviewed, CI-validated changes and zero-touch provisioning for new switches.
  • Build fabric observability using streaming telemetry/gNMI, sFlow, optics DOM, and buffer/drop counters integrated into Prometheus/Grafana with actionable alerting.
  • Run switch/NIC firmware upgrade campaigns with minimal workload disruption and serve as the escalation point for network incidents.
  • Design and operate dark fiber and DWDM capacity between sites, including latency budgeting and carrier/IX peering.
  • Architect AWS Direct Connect, Azure ExpressRoute, and GCP Interconnect links, VPC and Transit Gateway designs, and VPN failover paths.
  • Engineer WAN QoS and traffic paths for cross-cluster replication, dataset transfer, and checkpoint traffic.

Desired Qualifications

  • Experience procuring and operating dark fiber/DWDM.
  • Experience deploying BlueField DPUs or SmartNICs.
  • Experience supporting distributed training at 1,000+ GPU scale or multi-cluster/cross-site training.
  • Kubernetes networking for GPU serving, including CNI, SR-IOV, and Multus.
  • Expert certifications such as CCIE/JNCIE or NVIDIA networking certification in InfiniBand, Spectrum-X, or UFM.

About the Company

Sciforium

Serverless AI inference platform

Sciforium builds an AI infrastructure stack that makes large-scale AI cheaper and easier to deploy. It offers a serverless AI inference platform that exposes access to a mix of open-source and proprietary models through a single API, running on Sciforium’s own optimized AMD hardware. By owning the hardware, platform, and models, the company provides lower costs, faster performance, and better data privacy than shared-cloud setups. Its goal is to simplify and accelerate production-ready AI deployment while reducing costs and complexity for large-scale multimodal models, and it is exploring byte-native foundation models to improve efficiency.

Company size
11-50
Company stage
Not listed
Total funding
Not listed
Headquarters
San Francisco, CA
Founded
2024

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