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Lambda

Cloud-based GPU services for AI training

Senior Network Engineer

Network Engineering & AdministrationConfirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • Have 10+ years of experience in IT and networking, including 6+ years designing and operating production data center networks.
  • Have led the implementation of large production-scale networking projects.
  • Have experience managing next-generation firewalls, such as Fortigate.
  • Have experience with networking from cloud providers such as AWS, GCP, or OCI.
  • Be expert in CLOS/spine-and-leaf fabrics, EVPN/VXLAN, ECMP, BGP, and fast convergence techniques.
  • Be comfortable using the Linux command line and understand the Linux networking stack and internals.
  • Have strong automation skills in Python and Ansible, experience with network APIs, and experience with Git or similar source control systems.
  • Have production experience with multiple network equipment vendors, including Arista, Juniper, Cisco, Cumulus/SONiC, and Opengear.
  • Have experience with networking monitoring stacks, including Datadog, ClickHouse, Grafana, Prometheus, gNMI, and OTel.

Responsibilities

  • Help build and scale Lambda's high-performance cloud network.
  • Deploy and configure networking hardware for new and existing clusters.
  • Ensure high availability of the network through monitoring, failover, and redundancy.
  • Contribute to automation of network configuration management and operation.
  • Work with internal and external customers to resolve network-related issues.
  • Help deploy and maintain network monitoring and management tools.
  • Participate in day-two operations and the Network Engineering team's on-call rotation.

Desired Qualifications

  • Have knowledge of or experience maintaining software-defined networks (SDN).
  • Have experience automating network configuration within public clouds using tools such as Terraform, Ansible, or Salt.
  • Have hands-on experience with HPC/AI networking, including RoCEv2 and/or InfiniBand (congestion control, VLs, partitions), and GPUDirect RDMA concepts.
  • Have experience with DWDM technologies and SD-WAN.
  • Understand data center power, space, and cooling trade-offs and their impact on topology choices.
  • Have experience with virtualization technology such as ESXi and KVM, and VM management.
  • Have experience with load balancers such as F5 and NetScaler.

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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