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Lambda

Cloud-based GPU services for AI training

Principal Product Manager - Nvidia Technology Partnerships

Technical Product ManagementConfirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • Have 10+ years of product management experience, including time at a cloud provider, systems company, or silicon vendor.
  • Have owned a strategic technology partnership where two companies built things together rather than only bought and sold.
  • Can read a silicon and systems roadmap and judge what it means for a cloud business, including what is worth adopting and what is not.
  • Understand GPU platforms well enough to hold a real technical conversation with the people who design them.
  • Have made adoption decisions with meaningful capital and engineering cost behind them, and can explain the ones you got wrong.
  • Have run a program where an outside company's engineers and your own had to land the same thing at the same time.
  • Can say no to a powerful counterpart and keep the relationship.
  • Able to define iterative plans that move an organization from the current state towards the desired outcome.

Responsibilities

  • Stay far enough ahead of where NVIDIA's hardware and software is going that Lambda can plan against it rather than react to it.
  • Call which of NVIDIA's new hardware and software Lambda should productize into On-Demand GPU Instances, 1-Click Clusters, and private cloud deployments, and which to pass on, with the reasoning written down.
  • Take what Lambda adopts from NVIDIA the whole way into something customers can buy, including packaging, pricing, and launch criteria.
  • Be the single point of contact both companies call, route incoming requests to the right product and engineering owners, and close the loop.
  • Own the product side of what the two companies build together, including scope and what done means.
  • Work alongside Lambda's hardware product managers as new NVIDIA GPU generations move from evaluation into production, so platform decisions are informed by what is coming.
  • Turn what comes out of the relationship into requirements, user stories, and acceptance criteria Lambda engineering can build from without a translation layer.
  • Tell a partner what Lambda will not do and why, early and plainly enough that the relationship is better for it rather than worse.
  • Decide what a good adoption decision looks like before making it, then check whether it paid off.
  • Partner with the product managers who own compute, storage, networking, orchestration, and security, since most of what comes through the relationship lands in their areas.

Desired Qualifications

  • Experience with GPU or accelerator platforms, including how a new generation moves through qualification into production.
  • Experience evaluating a silicon or systems vendor's pre-release technology and deciding whether to build on it.
  • Experience treating data center power, cooling, or density as product inputs rather than someone else's problem.
  • Experience evaluating infrastructure software for adoption, including the build, buy, or partner decision.
  • Experience running a co-development program with an external partner.
  • Prior work at a GPU cloud, hyperscaler, or AI infrastructure company.
  • Deep hardware platform or infrastructure software experience at a cloud provider, silicon vendor, or systems company is highly desired.

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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