Lambda
From Lambda's careers page
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About the Role Customers come to Lambda for compute, and then decide whether to stay based on where their data lives.
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.
If you'd like to build the world's best AI cloud, join us.
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.
Customers come to Lambda for compute, and then decide whether to stay based on where their data lives. Weights, checkpoints, and training sets are the part of the workload that is expensive to move and painful to lose, so storage is where a GPU rental turns into a cloud a customer builds on.
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 reaches the GPUs. The domain covers the storage customers attach to an instance and the storage that outlives it, from local disk through block volumes to shared and object storage. It covers how customers get large datasets in and keep them current, and what throughput they can count on when thousands of processes read the same data at once or checkpoint into it together. It covers what customers are promised about durability, isolation, and key handling before an enterprise will put production on us. It covers how capacity is tiered, partitioned between shared and dedicated, metered, and priced, including what data transfer costs and who pays for it. Much of the stack is vendor-provided today, and deciding what Lambda should own instead is part of the work. You will work across these areas depending on which customer needs are burning hottest, 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 read and write 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 domains fast and reason rigorously from evidence. Deep storage platform experience at a cloud provider is highly desired.
If you have built storage products 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.
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 on data rather than what they asked for.
Decide What Matters: Rank and sequence the work across the storage portfolio, 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 capacity, performance, and data transfer are metered, priced, and packaged across On-Demand GPU Instances, 1-Click Clusters, and Lambda's private cloud offerings, including whether storage should bill alongside the GPUs it serves or on its own.
Deliver With Engineering: Work through the build with the storage teams and the vendors behind parts of the stack today, 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 a storage product needs, including durability, recovery, and what customers are told when data is at risk, 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 real workload performance 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 foundational infrastructure and with the teams that own compute, networking, orchestration, security, and commerce, so the pieces land as one product.
You
Have 7+ years of product management experience, including time on storage products at a hyperscaler, neocloud, or comparable cloud provider.
Have shipped storage that external customers ran production workloads on, in object, file, or block.
Understand the storage path well enough to reason with engineers about throughput, latency, consistency, and what happens to a workload when a device or a node fails.
Understand how data placement and read and write patterns show up as performance for distributed training and large-scale inference.
Have owned a product where durability and day two behavior mattered as much as launch, including recovery, capacity exhaustion, and what customers are told when something breaks.
Have owned pricing, packaging, or metering for an infrastructure product, including the data transfer side of it.
Can tell which parts of a hyperscaler playbook transfer to an AI cloud and which are cost without benefit.
Have worked with storage vendors and open source storage systems, and can decide what Lambda should build against what it should buy.
Able to define iterative plans that move an organization from the current state towards the desired outcome.
Communicate plainly, write well, and make decisions easier for people who do not all share the same context.
Nice to Have
Experience with object storage and S3-compatible interfaces, including the compatibility gaps that matter to customers porting from a hyperscaler.
Experience with parallel or shared file systems at high throughput, and with the quality of service controls that keep one tenant from taking the whole system.
Experience with block storage, snapshots, or the storage path behind instance boot and recovery.
Experience moving bulk data into a cloud, including resume, integrity verification, and throughput the customer can watch.
Experience with encryption at rest, key management, and tenant isolation in a multi-tenant storage system.
Familiarity with how storage traffic crosses a high performance fabric, like remote direct memory access or GPUDirect Storage.
Salary Range Information
The annual salary range for this position has been set based on market data and other factors. However, a salary higher or lower than this range may be appropriate for a candidate whose qualifications differ meaningfully from those listed in the job description.
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
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
Equal Opportunity Employer
Lambda is an Equal Opportunity employer. Applicants are considered without regard to race, color, religion, creed, national origin, age, sex, gender, marital status, sexual orientation and identity, genetic information, veteran status, citizenship, or any other factors prohibited by local, state, or federal law.
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