Staff Product Manager - Storage & Networking

Lambda; United States; Hybrid; USD 120,000 - 180,000; Full time

Job summary

Lambda, The Superintelligence Cloud, is hiring a Product Manager for the Foundational Infrastructure team. You will own how customer data lives on Lambda and how it gets to GPUs, spanning storage (weights, checkpoints, datasets) and networking (throughput, VPC, isolation).

You’ll work across spaces with other PMs to balance capabilities against cost while AI workloads move data patterns these primitives weren’t designed for.

Responsibilities

• Own storage and networking features for the Foundational Infrastructure team, shaping how data sits, moves, and is priced.
• Define data locality, durability, and performance requirements for weights, checkpoints, and datasets.
• Collaborate with other PMs and engineering teams to balance capabilities with cost as AI workloads evolve.

Job description

Lambda, The Superintelligence Cloud, is a leader in AI cloud infrastructure serving tens of thousands of customers. Our customers range from AI researchers to enterprises and hyperscalers. Lambda's mission is to make compute as ubiquitous as electricity and give everyone the power of superintelligence. One person, one GPU.

Note: This position requires presence in our Bellevue or San Francisco office location 4 days per week; Lambda's designated work from home day is currently Tuesday.

About the Role

Storage and networking are what turn graphics processing units (GPUs) into a cloud. Customers come to Lambda for compute, then decide whether to stay based on where their data lives and whether the network gets it to the GPUs fast enough to keep them busy. As a product manager on the Foundational Infrastructure team, you will own a meaningful part of defining where customer data lives on Lambda and how it gets to the GPUs. On storage, the domain covers where weights, checkpoints, and datasets sit, how customers get data in and keep it current, what performance they can count on when loading a model, and how capacity is metered and priced. On networking, it covers moving that data at the throughput training and inference demand, and the tenant isolation and virtual private cloud that enterprises require before they put production on us. It also covers how customers reach Lambda from their own networks, how traffic is balanced in front of serving endpoints, what egress they control, and how bandwidth is shared without one tenant starving another. You will most likely work across both spaces rather than sit in one of them, following what customers need most at the time, alongside the other product managers on the team. Most of this is the feature set a mature 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 that AI workloads move data in patterns these primitives were never designed for, and deciding where that difference should change the product is the more interesting half.

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 dom