Senior Platform Reliability Engineer (Fabric and Interconnect)

Location: Based in Australia or Singapore, with travel to Australian AI Factory sites as required

Company

hirify.global develops and operates energy-efficient AI infrastructure, including hirify.global AI Cloud and the proprietary AI FactoryOS platform.

What you will do

• Own the reliable operation, automation, and continuous improvement of GPU interconnect and high-performance network fabrics, including NVLink, NVSwitch, InfiniBand, and Spectrum-X.
• Build guarded automation and remediation tooling for fault isolation, fabric reconvergence, and software-driven recovery of AI clusters.
• Diagnose and tune performance across the interconnect stack, from application collective communication to physical link level, including RoCE and congestion control.
• Operate DPU-based host networking, including offload paths and driver and firmware compatibility.
• Plan and execute firmware upgrade waves, fabric expansions, capacity changes, staged rollouts, production verification, and rollback procedures.
• Lead major fabric incident recovery and vendor escalations, drive permanent fixes, and mentor engineers through runbooks and technical guidance.

Requirements

• 8+ years of experience in high-performance networking and systems engineering, including ownership of production network or interconnect infrastructure in a 24/7 environment.
• Deep operational experience with GPU interconnect fabrics, including NVLink and NVSwitch topology and failure diagnosis.
• Extensive experience with InfiniBand or RoCE-based Ethernet fabrics, routing internals, and congestion control tuning.
• Experience with DPU or SmartNIC-based host networking, offload paths, and driver and firmware coordination.
• Strong infrastructure automation and infrastructure-as-code skills, plus practical scripting or programming experience with Python, Go, or Bash.
• Experience with major incident response, on-call participation, engineering-level vendor escalation, production change control, and executable runbooks.

Nice to have

• Experience operating fabrics for large-scale distributed training or inference workloads.
• Experience with NVIDIA rack-scale or multi-node GPU systems and interconnect topology.
• Experience in a multi-tenant service provider, cloud, or colocation environment.
• Knowledge of data centre and hardware fundamentals, including cabling, optics, firmware management, and hardware fault workflows.
• Relevant vendor certification.

Culture & Benefits

• Permanent full-time employment.
• Participation in a shared after-hours escalation roster supporting a 24/7 function.
• High degree of autonomy, broad technical direction, and direct access to decision makers.
• Work focused on sustainable AI infrastructure, efficient computing, and large-scale GPU operations.