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Lambda

Cloud-based GPU services for AI training

Senior Storage Engineer

Platform EngineeringPosted on Nov 13
Full-time

About the Job

Requirements

  • At least 8 years of experience designing, building, and operating large-scale multi-petabyte storage production environments.
  • Familiarity with one or more storage solutions from Vast, Weka, DDN, NetApp, PureStorage, Dell, IBM, or HPE.
  • Knowledge of file, block, and object storage types.
  • Knowledge of storage network access protocols such as NFS, SMB, and POSIX-compliant protocols.
  • Knowledge of NVMe over Fabric storage transport protocols, including NVMe/TCP, NVMe/IB, or NVMe/RoCE.
  • Knowledge of storage performance via RDMA, GPUDirect Storage, and parallel file systems.
  • Knowledge of encryption, storage security, and multi-tenancy strategies.
  • Knowledge of storage data reduction and compression.
  • Knowledge of data protection concepts, backup strategies, and disaster recovery.
  • At least 5 years of experience with Infrastructure as Code, such as Terraform or Ansible.

Responsibilities

  • Author comprehensive performance and correctness validation plans for new storage tiers.
  • Draft Standard Operating Procedures and alerting and monitoring frameworks to scale operations.
  • Own the access-control lifecycle for storage solutions, managing vendor and customer permissions.
  • Lead technical communications with vendors and serve as a subject matter expert for customer success and complex account assistance.
  • Identify and act on signals for operational ownership, and mentor junior engineers in specific storage sub-domains.

Desired Qualifications

  • Experience with Kubernetes, including CSI and COSI drivers and CNI’s.
  • Deep understanding of storage performance.
  • Strong understanding of public cloud features, including SDN, block storage, distributed file systems, and identity management.
  • Experience deploying, operating, and maintaining Software Defined Storage.
  • Experience implementing open-source or commercial storage monitoring solutions.

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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