Compute is the product at Lambda. Customers come to us for graphics processing units (GPUs) they can get, hold, and run hard, and nearly everything else we sell depends on the compute layer working well.
As a product manager on the Compute team, you will own a meaningful part of defining the future of how we provide compute to our customers. The domain covers the shape of what customers rent, meaning instance families, sizes, and tenancy, across both GPUs and central processing units (CPUs). It covers how customers get capacity and hold onto it. It covers what they build production automation against, and how fast and predictably that behaves. It covers what happens once the workload is running, which means placement, performance isolation, hardware failure, maintenance, and the signals customers need to run their own operations. You will work across these areas depending on which customer needs are burning hottest.
Most of this is the feature set a mature compute cloud already has and Lambda does not have yet. A neocloud is not a hyperscaler, though, and copying that list wholesale is the wrong instinct. Part of the job is judgment about which of those capabilities matter here and which carry cost we should not pay. The other part is deciding where serving AI workloads well means building something the hyperscalers never needed.
Great product managers at Lambda are defined by three things: insight, influence, and execution. Insight means you look at the data, determine what it means for customers and business, and then figure out what to do about it. But, a great idea doesn’t mean anything in a vacuum. That is where influence comes in. Influence means you take that idea and get others to want to buy into it; you win over engineers, designers, executives, and partners without relying on authority. But a great idea that everyone is excited about doesn’t matter unless it is delivered to customers. Execution means you work with the right people to get the idea launched, then measure and iterate. We hire product managers who learn new domains fast and reason rigorously from evidence. Deep compute platform experience at a hyperscaler or neocloud, across both GPUs and CPUs, is highly desired.
If you have built compute primitives at a cloud provider and want to do it again somewhere the answers are not settled, we’d love to hear from you.
We value diverse backgrounds, experiences, and skills, and we are excited to hear from candidates who can bring unique perspectives to our team. If you do not exactly meet this description but believe you may be a good fit, please still apply and help us understand your readiness for this role.