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Lambda

Cloud-based GPU services for AI training

Optical Engineering Manager

Engineering LeadershipPosted on Nov 6
Full-timeDublin, IrelandRemote

About the Job

Requirements

  • At least 5 years of optical engineering management experience.
  • A proven track record of building and scaling engineering organizations that deliver mission-critical, high-performance cloud connectivity serving millions of users.
  • Ability to foster a data-driven, automation-first, high-velocity organization.
  • Familiarity with designing, building, scaling and operating large-scale networks.
  • Strong automation skills and a track record of accelerating business delivery velocity at large scale.
  • A track record of building high-performing teams in fast-moving, technically demanding environments.
  • Ability to translate ambiguous business and product goals into clear team priorities and executable engineering plans.
  • Strong judgment about when to go deep technically, when to delegate, and when to escalate.
  • A track record of successful project and product delivery in fast-paced, high-pressure environments.
  • Excellent written and verbal communication, including producing high-quality written artifacts to inform decision-making and gain approval on problems and solution opportunities.

Responsibilities

  • Own optical networking planning, delivery and operation end to end, and ensure capacity lands ahead of demand.
  • Build and lead the organization responsible for optical networking by hiring, developing and retaining engineers and their leaders, and designing team structure as the organization grows.
  • Set the multi-year technical strategy for optical capacity automation to ensure it scales continuously.
  • Convert manual processes into durable software, with the goal of a capacity pipeline that runs without heroics rather than a better-organized set of runbooks.
  • Own the operational standards for the organization, including how capacity work is planned, reviewed, shipped, measured and learned from when it goes wrong.
  • Manage through leads and senior individual contributors by providing regular one-on-ones, clear performance feedback, growth planning and sponsorship of meaningful work.
  • Partner with and support the Interconnect team, which negotiates, acquires and coordinates external connectivity.
  • Partner with Principal Engineers and technical leadership to maintain a high engineering bar across design, code quality and operational reliability.
  • Partner with Infrastructure, Supply Chain, Data Center Operations, Product and Finance to align capacity roadmaps, forecast demand and manage long-lead dependencies.
  • Represent the organization’s work and technical direction to senior leadership and, where appropriate, enterprise customers and partners.
  • Contribute to organization-wide engineering process improvements in planning, shipping and learning from incidents.

Desired Qualifications

  • Experience in GPU cloud, high-performance computing or AI/machine-learning infrastructure environments.
  • Familiarity with large-scale data center or cloud networking, including fabric build-out, capacity modelling or network supply chain.
  • Experience with infrastructure capacity forecasting and long-lead hardware planning.
  • Prior experience at a high-growth infrastructure or cloud company.

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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