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Lambda

Cloud-based GPU services for AI training

Engineering Manager - Kubernetes Platform, Lambda Kubernetes Service

Engineering LeadershipPosted on Nov 13
Full-time

About the Job

Requirements

  • Have 9 or more years of software engineering experience, including 3 or more years leading engineering teams.
  • Have built or operated Kubernetes platforms, managed Kubernetes services, or container infrastructure.
  • Bring strong technical judgment across Kubernetes, platform APIs, operators, and cluster lifecycle.
  • Be able to guide tradeoffs across extensibility, compatibility, security, reliability, and customer experience.
  • Build strong teams and work effectively across engineering, product, and infrastructure organizations.
  • Communicate clearly and turn ambiguous needs into pragmatic plans and results.
  • Hold a BS, MS, or PhD in Computer Science or a related field, or have equivalent practical experience.

Responsibilities

  • Build and lead the engineering team; hire strong engineers, develop technical leaders, and set a high bar for ownership and quality.
  • Define the technical strategy and roadmap for LKS platform capabilities and Kubernetes runtime.
  • Deliver a reliable baseline cluster experience using upstream, CNCF-conformant Kubernetes.
  • Own the integration and lifecycle of managed add-ons, including qualification, compatibility, releases, and customer choice.
  • Build Kubernetes capabilities for AI workloads, starting with Slurm through Slinky and evaluating options such as Volcano and LLMD as needs grow.
  • Partner with the Hosted Control Plane team on cluster lifecycle, upgrades, release readiness, and incident response.
  • Work across Lambda infrastructure and product teams to deliver secure, supportable integrations.
  • Communicate progress, risks, and customer impact clearly to leadership and partner teams.

Desired Qualifications

  • Experience with GPU operators, AI infrastructure, or GPU-aware Kubernetes.
  • Experience with Slurm, Slinky, Volcano, LLMD, Kueue, Ray, or similar orchestration systems.
  • Experience with Kubernetes networking, storage, upgrades, or managed add-on catalogs.

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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