Staff Network Engineer, Deployment

Crusoe · Data AI · San Francisco, CA - US · Cloud Engineering

Crusoe is an AI infrastructure company building and operating data centers for AI workloads. This role focuses on the physical and logical implementation of their global network infrastructure, ensuring new data centers and edge sites are brought online efficiently. Responsibilities include leading network build-outs, translating designs into implementation plans, testing and commissioning, automating deployment, managing on-site partners, and inventory management.

What you'd actually do

  1. Lead the end-to-end deployment of network infrastructure in new and existing data centers, from initial rack/stack oversight to final hand-off.
  2. Take high-level designs from the Network Development team and translate them into site-specific implementation plans, cable maps, and configuration templates.
  3. Perform rigorous "Burn-in" testing and site acceptance testing (SAT) for new network clusters, ensuring zero-defect handovers to the Operations team.
  4. Use Python, Ansible, and ZTP (Zero Touch Provisioning) to automate the staging and configuration of hundreds of network devices simultaneously.
  5. Coordinate with remote hands, structured cabling vendors, and data center providers to ensure physical layer standards (fiber paths, power requirements, and cooling) meet Crusoe’s stringent HPC requirements.

Skills

Required

  • 8+ years of experience in network engineering with a heavy focus on large-scale data center deployments and infrastructure projects.
  • Expert knowledge of structured cabling (SMF/MMF, MPO/MTP), optical transceivers (400G/800G), and data center power/cooling requirements.
  • Hands-on experience configuring Arista (EOS), Juniper (Junos), and NVIDIA/Mellanox platforms in a leaf-spine architecture.
  • Solid understanding of BGP, EVPN-VXLAN, and LLDP as they relate to large-scale fabric provisioning.
  • Proficiency in Python and Ansible for automating repetitive deployment tasks and validating configuration state.
  • Proven ability to manage multiple complex projects simultaneously across different time zones and physical locations.
  • Ability to diagnose complex physical layer and link-layer issues using OTDRs, light meters, and packet captures.
  • Bachelor’s degree in a technical field or equivalent practical experience in hyperscale or ISP environments.

What the JD emphasized

  • high-performance compute (HPC)
  • GPU-based AI infrastructure
  • network infrastructure
  • data centers
  • AI infrastructure
  • AI workloads
  • AI compute
  • AI cloud
  • AI strategies