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. 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. About the Role Lambda works closely with NVIDIA as a partner and investor, and builds its cloud on their hardware. A partner, an investor, and a supplier are three different conversations, and this role holds all of them from the product side. As a product manager on the Foundational Infrastructure team, you stay current on where NVIDIA's hardware and software roadmap is heading and decide what Lambda should do about it. Some of what they build we should adopt and turn into products. Some of it we should pass on, and saying so early and clearly saves the company a great deal of work. You make that call with the evidence behind it, then route what we do take on to the product and engineering owners who will carry it. Holding it together means being the person both companies call, and making sure a request that arrives in one conversation reaches the right owner instead of sitting. Large joint engineering programs run through this role too. When the two companies build something together, you own the product side of it, including scope and what done means. That work is cross-functional by nature and rarely sits inside one team. 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 hardware platform or infrastructure software experience at a cloud provider, silicon vendor, or systems company is highly desired. If you have held a relationship like this before and know that the hard part is the judgment rather than the meetings, 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. What You'll Do - Track the Roadmap: Stay far enough ahead of where NVIDIA's hardware and software is going that Lambda can plan against it rather than react to it. - Decide What Lambda Takes From NVIDIA: Call which of their new hardware and software Lambda should productize into On-Demand GPU Instances, 1-Click Clusters, and private cloud deployments, and which to pass on, with the reasoning written down. - Turn Adoption Into Product: Take what Lambda adopts from NVIDIA the whole way into something customers can buy, including packaging, pricing, and launch criteria. - Own the Relationship: Be the single point of contact both companies call. Route incoming requests to the right product and engineering owners and close the loop rather than letting things sit. - Run the Joint Work: Own the product side of what the two companies build together, including scope and what done means. - Bring the Partner View to Hardware: Work alongside Lambda's hardware product managers as new NVIDIA GPU generations move from evaluation into production, so the platform decisions they own are made with the best picture of what is coming. - Write the Definition: Turn what comes out of the relationship into requirements, user stories, and acceptance criteria Lambda engineering can build from without a translation layer. - Say No Clearly: Tell a partner what Lambda will not do and why, early and plainly enough that the relationship is better for it rather than worse. - Measure the Call: Decide what a good adoption decision looks like before you make it, then go back and check whether it paid off. - Work as a Group: Partner with the product managers who own compute, storage, networking, orchestration, and security, since most of what comes through this relationship lands in their areas. You - Have 10+ years of product management experience, including time at a cloud provider, systems company, or silicon vendor. - Have owned a strategic technology partnership where two companies built things together rather than only bought and sold. - Can read a silicon and systems roadmap and judge what it means for a cloud business, including what is worth adopting and what is not. - Understand GPU platforms well enough to hold a real technical conversation with the people who design them. - Have made adoption decisions with meaningful capital and engineering cost behind them, and can explain the ones you got wrong. - Have run a program where an outside company's engineers and your own had to land the same thing at the same time. - Can say no to a powerful counterpart and keep the relationship. - 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 GPU or accelerator platforms, including how a new generation moves through qualification into production. - Experience evaluating a silicon or systems vendor's pre-release technology and deciding whether to build on it. - Experience treating data center power, cooling, or density as product inputs rather than someone else's problem. - Experience evaluating infrastructure software for adoption, including the build, buy, or partner decision. - Experience running a co-development program with an external partner. - Prior work at a GPU cloud, hyperscaler, or AI infrastructure company. 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. 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: lambda.ai - 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.

Requirements

Must-haves

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.
If you have held a relationship like this before and know that the hard part is the judgment rather than the meetings, we'd love to hear from you.
Track the Roadmap: Stay far enough ahead of where NVIDIA's hardware and software is going that Lambda can plan against it rather than react to it.
Turn Adoption Into Product: Take what Lambda adopts from NVIDIA the whole way into something customers can buy, including packaging, pricing, and launch criteria.
Own the Relationship: Be the single point of contact both companies call. Route incoming requests to the right product and engineering owners and close the loop rather than letting things sit.
Run the Joint Work: Own the product side of what the two companies build together, including scope and what done means.
Write the Definition: Turn what comes out of the relationship into requirements, user stories, and acceptance criteria Lambda engineering can build from without a translation layer.
Say No Clearly: Tell a partner what Lambda will not do and why, early and plainly enough that the relationship is better for it rather than worse.
Measure the Call: Decide what a good adoption decision looks like before you make it, then go back and check whether it paid off.
Work as a Group: Partner with the product managers who own compute, storage, networking, orchestration, and security, since most of what comes through this relationship lands in their areas.
Have 10+ years of product management experience, including time at a cloud provider, systems company, or silicon vendor.
Have owned a strategic technology partnership where two companies built things together rather than only bought and sold.
Can read a silicon and systems roadmap and judge what it means for a cloud business, including what is worth adopting and what is not.
Understand GPU platforms well enough to hold a real technical conversation with the people who design them.
Have made adoption decisions with meaningful capital and engineering cost behind them, and can explain the ones you got wrong.
Have run a program where an outside company's engineers and your own had to land the same thing at the same time.
Can say no to a powerful counterpart and keep the relationship.

Nice-to-haves

Experience with GPU or accelerator platforms, including how a new generation moves through qualification into production.
Experience evaluating a silicon or systems vendor's pre-release technology and deciding whether to build on it.
Experience treating data center power, cooling, or density as product inputs rather than someone else's problem.
Experience evaluating infrastructure software for adoption, including the build, buy, or partner decision.
Experience running a co-development program with an external partner.
Prior work at a GPU cloud, hyperscaler, or AI infrastructure company.

Application prep

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Keywords to mirror

aireactgomachine learning

Platform: ashby
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Benefits

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Dental

Vision

401k

Paid Time Off

Wellness