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Lambda

Cloud-based GPU services for AI training

Network Infrastructure Delivery Manager

Project ManagementUpdated on Nov 13
Full-time

About the Job

Requirements

  • Experience leading network implementation in data centers, at the network edge, or across carrier networks, including changes to live infrastructure.
  • Hands-on experience with fiber routes, cross-connects, equipment installation, and physical site readiness.
  • Ability to read network designs, review physical and logical test results, and identify gaps between approved scope and delivered work.
  • Experience coordinating engineers, site teams, and suppliers through concurrent projects, including stalled deliveries and service escalations.
  • Working knowledge of optical transport, lit services, wavelengths, and dark fiber, including acceptance requirements.
  • Experience maintaining close-out packages and drawings, reporting risks, and resolving issues.

Responsibilities

  • Support Engineering during design planning by checking supplier lead times, equipment availability, and site readiness, and keep implementation plans aligned as designs change.
  • Drive deployments, refreshes, end-of-life replacements, labs, and special projects; coordinate Engineering, Product, HPC, Solutions Engineering, and account technical teams around implementation needs.
  • Coordinate fiber paths inside and outside facilities, site access, optics, and local technicians with Data Center Operations, and place and track approved cross-connect orders.
  • Maintain schedules, dependencies, and recovery plans, and keep the project management office informed of network milestones and their impact on overall deployments.
  • Own implementation drawings for the network edge, working with Network Engineering on the design plan and bringing technical changes to Engineering for approval.
  • Own contracted circuit delivery through activation; visit sites, coordinate physical and logical validation, and resolve gaps against the approved design.
  • Build close-out packages with as-built drawings, meet-me-room records, test results, and approvals, including service records and support contacts for Operations.
  • Manage service changes, upgrades, disconnections, and carrier escalations with Engineering and Operations; follow supplier issues through restoration and documented root cause.
  • Provide Supply Chain with service dates and records for billing corrections or credits, and obtain Supply Chain approval before proceeding with new commercial terms.
  • Develop implementation runbooks, playbooks, and acceptance checklists, using deployment lessons to improve subsequent builds.
  • Administer network delivery tools and supplier portals, and maintain shared inventory, drawings, and project records so teams work from the same source of truth.
  • Raise risks early, work with responsible teams to remove blockers, and track improvements through completion.

Desired Qualifications

  • Experience delivering labs, proofs of concept, new products, or equipment refreshes.
  • Ability to interpret optical time-domain reflectometer (OTDR) traces.
  • Experience administering NetBox, Jira, or carrier portals.

About the Company

Lambda

Cloud-based GPU services for AI training

Lambda Labs provides cloud-based GPU services for AI training and inference via its AI Developer Cloud, powered by NVIDIA GH200 Grace Hopper hardware to efficiently train large language models and generative AI. Customers access on-demand or reserved GPUs and are billed by the hour (for example, NVIDIA H100 at about $1.99/hour); it also offers Lambda Echelon, where clients own infrastructure but pay a monthly hosting and support fee, and Lambda Stack, a software suite that helps ML teams install and upgrade tools like PyTorch, TensorFlow, CUDA, cuDNN, and NVIDIA drivers. The company differentiates itself through competitive pricing, available high-end hardware, and an integrated ML software stack that streamlines workflows. Its goal is to enable scalable, affordable AI development and deployment by providing flexible GPU access, reliable hosting, and easy software deployment for teams working with large models.

Company size
501-1,000
Company stage
Late Stage VC
Total funding
$9.1B
Headquarters
San Jose, CA
Founded
2012

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