Staff Product Manager - Compute
Bellevue · San Francisco Office (Fremont St) · 7+ years experience · Full-time · Hybrid · Posted Oct 9, 2026
At a glance
- Experience
- 7+ years experience
- Employment
- Full-time
- Workplace
- Hybrid
- Location
- Bellevue · San Francisco Office (Fremont St)
About Lambda
- - Founded in 2012, with 500+ employees, and growing fast
- - Our investors notably include TWG Global, US Innovative Technology Fund (USIT), Andra Capital, SGW, Andrej Karpathy, ARK Invest, Fincadia Advisors, G Squared, In-Q-Tel (IQT), KHK & Partners, NVIDIA, Pegatron, Supermicro, Wistron, Wiwynn, Gradient Ventures, Mercato Partners, SVB, 1517, and Crescent Cove
- - We have research papers accepted at top machine learning and graphics conferences, including NeurIPS, ICCV, SIGGRAPH, and TOG
- - Our values are publicly available: https://lambda.ai/careers
- - We offer generous cash & equity compensation
- - Health, dental, and vision coverage for you and your dependents
About the role
- Compute is the core product at Lambda, driving everything from instance families and sizes to tenancy across GPUs and CPUs.
- As a Staff Product Manager on the Compute team, you will define how compute is provided, managed, and scaled for customers.
- You will make strategic judgments on whether to adopt mature cloud capabilities or build custom solutions tailored specifically for AI workloads.
Responsibilities
- Find the Real Problem: Get close to customers, deals, escalations, and support on On-Demand GPU Instances and 1-Click Clusters, and work out what is actually blocking them rather than what they asked for.
- Decide What Matters: Rank the work and sequence it, and be honest about what Lambda is not doing this year and why.
- Write the Definition: Produce requirements, user stories, and acceptance criteria precise enough that engineering builds from them without a translation layer.
- Price and Package It: Decide how your part of compute is priced, packaged, and committed to across NVIDIA GPU generations, and how customers compare the options.
- Deliver With Engineering: Work through the build with the compute and control plane teams, make the tradeoff calls that come up mid-flight, and keep scope honest against the date.
- Land the Launch: Set launch criteria that cover the operational readiness an infrastructure product needs, and get documentation, pricing, sales, and support in place before it goes live rather than after.
- Measure and Iterate: Define what success looks like before launch, then go find out whether it happened, using adoption and utilization rather than opinion.
- Make the Call Stick: Take a position on contested tradeoffs, write it down well enough that people can disagree with it precisely, and keep owning the decision after it is made.
- Work as a Group: Partner with the other product managers on compute and with the teams that own storage, networking, orchestration, and commerce, so the pieces land as one product.
Requirements
- Have 7+ years of product management experience, including time on a compute platform at a hyperscaler, neocloud, or comparable cloud provider.
- Have shipped compute primitives that external customers used at scale, like instance types, capacity products, provisioning interfaces, or placement and isolation controls.
- Understand GPU and CPU infrastructure well enough to reason with engineers about launch paths, hardware failure modes, placement, and performance.
- Have owned a product where day two behavior mattered as much as launch, including failure handling, maintenance, and what customers are told when something breaks.
- Have owned pricing, packaging, or commitment terms for an infrastructure product.
- Can tell which parts of a hyperscaler playbook transfer to an AI cloud and which are cost without benefit.
- Can reason about utilization and capacity economics, and make the call when customer flexibility and hardware utilization pull against each other.
- Able to define iterative plans that move an organization from the current state towards the desired outcome.
- Can turn ambiguous customer, technical, and commercial inputs into product definition that multiple teams execute against.
- Communicate plainly, write well, and make decisions easier for people who do not all share the same context.
Preferred qualifications
- Experience with interruptible or preemptible capacity, or with reservations and committed use products.
- Experience with the surfaces customers automate against, like public APIs, infrastructure as code providers, machine images, or snapshots.
- Experience with hardware failure handling across a large hardware footprint, like spare pools, node replacement, or fault containment.
- Experience with performance isolation and placement on shared hardware, including what a provider can honestly guarantee.
- Experience with bare metal or dedicated host products, including isolation between tenants.
- Experience with distributed training or large-scale inference workloads and what they demand of the compute layer.
Benefits
- Generous cash & equity compensation
- Health, dental, and vision coverage for you and your dependents
- Wellness and commuter stipends for select roles
- 401k Plan with 2% company match (USA employees)
- Flexible paid time off plan that we all actually use
Workplace
- 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.
Skills
- GPUs
- CPUs
- On-Demand GPU Instances
- 1-Click Clusters
- Compute Primitives
- Public APIs
- Infrastructure As Code
- Machine Images
- Snapshots
- Distributed Training
