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Lambda

Cloud-based GPU services for AI training

Data Center Operations System Engineer 3

System AdministrationUpdated on Nov 3
Full-timeColumbus, OH, USA

About the Job

Requirements

  • Have strong prior experience with critical infrastructure systems supporting data centers, including power distribution, airflow management, environmental monitoring, capacity planning, data center infrastructure management software, structured cabling, and cable management.
  • Be familiar with carrier dedicated internet access circuit testing and turn-ups, fiber testing, and troubleshooting.
  • Have basic knowledge of cable optics and their different types of use.
  • Have a solid understanding of single- and three-phase power theories.
  • Understand power distribution unit balancing and why it is important.
  • Be familiar with multiple cable media types and their uses.
  • Know cold-aisle and hot-aisle containment.
  • Have a solid understanding of server hardware and the boot process.
  • Be able to structure, collaborate on, and iteratively improve complex maintenance methods of procedure.
  • Be action-oriented and willing to train junior staff on best practices.
  • Be willing to travel for bring-up of new data center locations as needed.

Responsibilities

  • Ensure new server, storage, and network infrastructure is properly racked, labeled, cabled, and configured.
  • Troubleshoot hardware and software issues in advanced GPU and networking systems.
  • Document and update data center layouts and network topology in data center infrastructure management software.
  • Work with supply chain and manufacturing teams to ensure timely system deployment and develop project plans for large-scale deployments.
  • Manage a parts depot inventory and track equipment through the delivery, storage, staging, deployment, and handoff process in each data center.
  • Partner with hardware support teams to resolve data center hardware incidents requiring higher-level troubleshooting, report on incidents, and disseminate solutions to the wider operations organization.
  • Work with the return merchandise authorization team to ensure faulty parts are returned and replacements are ordered.
  • Follow installation standards and documentation for equipment placement, labeling, and cabling to promote consistency and discoverability across data centers.
  • Work with product management, support, and other teams to align operational capabilities with company goals.
  • Translate business priorities into technical and operational requirements.
  • Support cross-functional projects where infrastructure plays a critical role.

Desired Qualifications

  • Have 2+ years of experience with critical infrastructure systems supporting data centers, including power distribution, airflow management, environmental monitoring, capacity planning, data center infrastructure management software, structured cabling, and cable management.
  • Have experience with or knowledge of network topology, network configurations, and 400-gigabit InfiniBand architectures.
  • Have experience with or knowledge of DDP or SCM cluster storage systems.
  • Have 3+ years of experience working with and reporting from ticketing systems such as JIRA and Zendesk.
  • Have advanced Linux administration experience.
  • Have experience with high-performance computing GPU systems, air-cooled or water-cooled, especially NVIDIA NVL72.

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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