Staff Product Manager – Product Operations (ProdOps) at Lambda

San Francisco, California, United States

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$275,000 – $484,000Compensation

Senior (5 to 8 years), Expert & Leadership (9+ years)Experience Level

Full TimeJob Type

UnknownVisa

AI, Cloud Computing, TechnologyIndustries

Requirements

8+ years of experience in product management, product operations, or program management, with at least 3+ years in a senior or staff-level role
Deep understanding of product development lifecycles, agile practices, and portfolio management
Experience in AI infrastructure or AI platforms is ideal
Demonstrated success managing internal tools, workflows, or operational platforms as a product owner
Proven ability to drive alignment and execution across complex, matrixed organizations
Strong analytical skills — able to build models, interpret data, and translate insights into scalable frameworks
Excellent written and verbal communication skills; adept at synthesizing complex information for executive audiences
Experience with modern product and operations toolchains (e.g., Jira, Productboard, Aha)

Responsibilities

Lead the definition and continuous improvement of product operating rhythms — roadmap planning, goal setting, prioritization, and execution frameworks
Establish and refine processes that increase the velocity, quality, and alignment of product delivery
Partner with product and engineering leadership to ensure the right balance between strategic initiatives, technical investment, and innovation
Act as the product manager for internal product operations tools and systems, including roadmap management platforms, capacity planning models, OKR systems, and collaboration tools
Define requirements, manage vendor integrations, and build automation to reduce manual effort and improve transparency
Partner with data and engineering teams to develop internal dashboards and analytics capabilities that surface insights on delivery, utilization, and team health
Be the connective tissue between Product Management and multiple cross-functional teams, including but not limited to Engineering, Marketing, Sales, Program Management, Operations, and Finance through structured operational cadences and effective communication channels
Lead product team business reviews, planning sessions, and retrospectives
Create clarity by ensuring decision-making, status, and outcomes are documented and visible across teams
Define and operationalize key metrics for product execution health — velocity, delivery predictability, roadmap balance, and team capacity
Translate data and qualitative feedback into actionable insights that inform strategy and operations improvements
Champion a culture of accountability and learning through process retrospectives and experimentation

Skills

Key technologies and capabilities for this role

Product OperationsRoadmap PlanningCapacity PlanningOKR SystemsPrioritization FrameworksInternal ToolingCross-Functional AlignmentProduct Strategy

Questions & Answers

Common questions about this position

What is the salary range for the Staff Product Manager – Product Operations role?

The salary range is $275K - $484K.

Is this position remote or hybrid, and what are the office requirements?

This is a hybrid position requiring presence in the San Francisco/San Jose/Seattle office 4 days per week, with Tuesday designated as the work-from-home day.

What key skills and experiences are required for this Staff Product Manager role?

The role requires strong skills in product operations strategy, process definition and improvement, internal tooling product management, cross-functional alignment, and measurement with data insights.

What is the company culture like at Lambda?

Lambda focuses on building the world's best deep learning cloud with a mission to make compute ubiquitous like electricity and provide AI access to everyone, emphasizing collaboration with senior leaders across product, engineering, and company-wide teams.

What makes a strong candidate for this Staff Product Manager position?

A strong candidate will have experience operating as a force multiplier in product organizations, with expertise in designing operational processes, managing internal tools, leading cross-functional alignment, and driving data-informed improvements at scale.

Lambda

Cloud-based GPU services for AI training

About Lambda

Lambda Labs provides cloud-based services for artificial intelligence (AI) training and inference, focusing on large language models and generative AI. Their main product, the AI Developer Cloud, utilizes NVIDIA's GH200 Grace Hopper™ Superchip to deliver efficient and cost-effective GPU resources. Customers can access on-demand and reserved cloud GPUs, which are essential for processing large datasets quickly, with pricing starting at $1.99 per hour for NVIDIA H100 instances. Lambda Labs serves AI developers and companies needing extensive GPU deployments, offering competitive pricing and infrastructure ownership options through their Lambda Echelon service. Additionally, they provide Lambda Stack, a software solution that simplifies the installation and management of AI-related tools for over 50,000 machine learning teams. The goal of Lambda Labs is to support AI development by providing accessible and efficient cloud GPU services.

San Jose, CaliforniaHeadquarters

2012Year Founded

$372.6MTotal Funding

DEBTCompany Stage

AI & Machine LearningIndustries

201-500Employees

Risks

Nebius' holistic cloud platform challenges Lambda's market share in AI infrastructure.

AWS's 896-core instance may draw customers seeking high-performance cloud solutions.

Existential crisis in Hermes 3 model raises concerns about Lambda's AI model reliability.

Differentiation

Lambda offers cost-effective Inference API for AI model deployment without infrastructure maintenance.

Nvidia HGX H100 and Quantum-2 InfiniBand Clusters enhance Lambda's AI model training capabilities.

Lambda's Hermes 3 collaboration showcases advanced AI model development expertise.

Upsides

Inference API launch attracts enterprises seeking low-cost AI deployment solutions.

Nvidia HGX H100 clusters provide competitive edge in high-performance AI computing.

Strong AI cloud service growth indicates rising demand for Lambda's GPU offerings.

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