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Lambda is a leader in AI cloud infrastructure serving tens of thousands of customers, from AI researchers to enterprises and hyperscalers. The company's mission is to make compute as ubiquitous as electricity and give everyone access to superintelligence.
As a Staff Product Manager on the Compute team, you will own a meaningful portion of defining the future of how Lambda provides compute to customers. The domain spans instance families, sizes, and tenancy models across both GPUs and CPUs; capacity acquisition and retention; production automation interfaces; and operational behavior including placement, performance isolation, hardware failure handling, and maintenance.
Most of this feature set exists in mature compute clouds but not yet at Lambda. The role requires judgment about which hyperscaler capabilities matter for an AI-focused neocloud and which carry unnecessary cost, as well as identifying where serving AI workloads well means building something hyperscalers never needed.
Key responsibilities include: getting close to customers and support to identify real blockers; ranking and sequencing work honestly; writing precise requirements and acceptance criteria; deciding pricing, packaging, and commitment strategies; working through builds with engineering teams and making mid-flight tradeoff calls; setting launch criteria and ensuring operational readiness; defining success metrics and measuring post-launch adoption and utilization; taking positions on contested tradeoffs and owning decisions; and partnering across product and infrastructure teams.
Great product managers at Lambda are defined by insight (looking at data and determining what it means), influence (winning buy-in from engineers, designers, executives, and partners without authority), and execution (delivering to customers and iterating based on evidence).
Requirements:
- 7+ years of product management experience, including time on a compute platform at a hyperscaler, neocloud, or comparable cloud provider
- Shipped compute primitives used at scale by external customers (instance types, capacity products, provisioning interfaces, placement and isolation controls)
- Deep understanding of GPU and CPU infrastructure to reason with engineers about launch paths, hardware failure modes, placement, and performance
- Owned a product where day-two behavior mattered as much as launch (failure handling, maintenance, customer communications)
- Owned pricing, packaging, or commitment terms for an infrastructure product
- Ability to discern which hyperscaler playbook elements transfer to AI cloud and which do not
- Can reason about utilization and capacity economics and make calls when customer flexibility and hardware utilization conflict
- Can define iterative plans moving an organization from current state to desired outcome
- Can turn ambiguous customer, technical, and commercial inputs into executable product definition
- Communicate plainly, write well, and make decisions easier for people without shared context
Nice to have:
- Experience with interruptible or preemptible capacity, or reservations and committed use products
- Experience with customer automation surfaces (APIs, infrastructure as code, machine images, snapshots)
- Experience with hardware failure handling at scale (spare pools, node replacement, fault containment)
- Experience with performance isolation and placement on shared hardware
- Experience with bare metal or dedicated host products
- Experience with distributed training or large-scale inference workloads
