Principal Product Manager, Lambda

Inference

$300-385k

+ Equity

Kubernetes

Expert level

San Francisco Bay Area

Deep learning infrastructure company

Job no longer available

Deep learning infrastructure company

501-1000 employees

B2CB2BArtificial IntelligenceEnterpriseManufacturingDeep TechMachine LearningCloud Computing

Job no longer available

$300-385k

+ Equity

Kubernetes

Expert level

San Francisco Bay Area

501-1000 employees

B2CB2BArtificial IntelligenceEnterpriseManufacturingDeep TechMachine LearningCloud Computing

Company mission

To make compute as ubiquitous as electricity and give everyone in America the power of superintelligence.

Job

Company

Role

Who you are

Are an experienced product leader: you have 10+ years in product management, including 5+ building AI/ML platforms
Drive adoption & impact: you have a track record of launching and scaling products that saw real usage and commercial success
Know how the technology actually works: from vLLM, to Kubernetes, to model distillation and quantization, to workflows and agents
Can turn ambiguity into action: you make decisions fast and align teams quickly. You are able to balance long term directions with the requirements of an incredibly dynamic marketplace
Stay plugged into the AI/ML ecosystem: you’re curious, passionate, well-networked, and know where the field is headed
Have a strong technical foundation: CS, Engineering, Math degree (or equivalent experience)

What the job involves

As Principal Product Manager, you will own the strategy, roadmap, and execution for Lambda’s inference platform
You’ll work across engineering, research, and business teams to build and scale our inference platform – from model evaluation and deployment to optimization, observability, and performance tuning
You will shape how organizations – from early-stage startups to Fortune 500 companies – deploy and run models at scale, making inference faster, cheaper, and more efficiently
Define the vision: what does world-class inference look like? Chart the path forward by keeping your finger on the pulse of cutting edge AI trends and collaborating with our engineering and research teams
Execute and deliver: Establish and ensure timely delivery of key milestones. Resolve ambiguities and obstacles that arise in new, undefined areas of the product
Engage deeply with AI developers: understand their workflows, pain points, and needs to shape the product
Build strategic partnerships: identify key AI ecosystem players for integrations and collaboration
Align cross-functionally: work closely with company leadership, engineering, research, sales, and marketing to ensure success

Our take

Lambda is a leading provider of deep learning GPU cloud services and computing hardware, founded to aid in providing a solution to the difficult problem of teaching machines how to see and learn.

Their customers include Apple, Intel, Microsoft, Linkedin, and Fortune 500 companies like Microsoft and Amazon Research, so it's no coincidence that Lambda GPU Cloud ranks number three on a google search for 'GPU cloud', behind only NVIDIA and Google itself.

After a successful 2025, the company is now looking at an initial public offering in the second half of 2026. This comes after the company signed a multi-billion-dollar contract with Microsoft to provide the hyperscaler with access to its GPUs, and it's also believed to have signed a $1.5B lease-back deal with Nvidia. This, coupled with significant funding put Lambda in a very good position for future growth.

Freddie

Company Specialist at Welcome to the Jungle

Insights

Top investors

26% employee growth in 12 months

Glassdoor (3.5)

Company

Funding (last 2 of 6 rounds)

Nov 2025

$1.5bn

SERIES E

Feb 2025

$480m

SERIES D

Total funding: $2.4bn

Company benefits

Health, dental vision
401k match
5 sick days and 12 paid holidays
Flexible PTO
Paid parental, medical, and caregiver leave

Company HQ

Financial District, San Francisco, CA

Leadership

Michael Balaban

(Co-Founder & CTO)

Graduating with a BS in Discrete Mathematics and Computer Science, Michael is experienced in software engineering and previously worked for Nextdoor as part of its infrastructure team.

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