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Lambda

Cloud-based GPU services for AI training

Data Center Operations System Engineer 2

System AdministrationConfirmed live in the last 24 hours
Full-timeDallas, TX, USA

About the Job

Requirements

  • Familiarity 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.
  • Ability to follow instructions and pay attention to detail.
  • Willingness to travel for bring-up of new data center locations.

Responsibilities

  • Ensure new server, storage, and network infrastructure is properly racked, labeled, cabled, and configured.
  • Troubleshoot hardware and software issues in advanced systems.
  • Document data center layout and network topology in data center infrastructure management software.
  • Work with supply chain and manufacturing teams to ensure timely system deployment and 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.
  • Work with the hardware support team to resolve data center infrastructure-related support tickets.
  • Work with the return merchandise authorization team to return faulty parts and order replacements.
  • Follow installation standards and documentation for placement, labeling, and cabling to support consistency and discoverability across data centers.

Desired Qualifications

  • Experience troubleshooting server hardware.
  • Experience with or knowledge of network topology.
  • Familiarity with ticketing systems such as JIRA and Zendesk.
  • Experience with Linux administration.
  • Experience working in large-scale distributed data center environments.
  • Experience with Supermicro and Nvidia hardware.

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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