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CoreWeave · Product & Design · Staff+ · Posted 2026-08-07

Staff Product Manager - Core Compute Services

CoreWeave · Livingston, NJ / New York, NY / Sunnyvale, CA / Bellevue, WA

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CoreWeave is The Essential Cloud for AI™. Built for pioneers by pioneers, CoreWeave delivers a platform of technology, tools, and teams that enables innovators to build and scale AI with confidence. Trusted by leading AI labs, startups, and global enterprises, CoreWeave combines superior infrastructure performance with deep technical expertise to accelerate breakthroughs and turn compute into capability. Founded in 2017, CoreWeave became a publicly traded company (Nasdaq: CRWV) in March 2025. Learn more at  www.coreweave.com .

What You'll Do:

The core compute team builds and operates the compute infrastructure that underpins all the AI workloads running on CoreWeave. We turn accelerated servers into reliable, isolated, API-addressable compute that customers can build on, and we keep it healthy and current for the life of their workloads. Everything above us depends on it: customers running training, inference, agents, and sandboxes directly on our compute, and internal teams building higher-level services, including CoreWeave Kubernetes Service (CKS), on top of our primitives.

About the role:

As Staff Product Manager for Core Compute, you'll be responsible for driving the roadmap and strategy for the compute layer that everything else at CoreWeave sits on: how compute gets allocated and delivered, how it stays healthy and current, and how it is isolated and made consumable through APIs that both external customers and internal teams build against.

You will be working on the foundation of the compute stack to both evolve what already exists, and help define the next generation of compute services for AI.

In this role, you will:

Lead the product strategy, roadmap, and execution for CoreWeave's core compute services, including instance lifecycle, node images and system software currency, hardware health and remediation, tenancy and isolation, and the capacity and reservation semantics customers buy against

Serve two customer sets at once: external customers consuming compute directly, and internal teams building higher-level services, including CKS, on the same primitives

Partner with engineering, sales, finance, and marketing teams to ensure alignment on goals, product details, effective product launches, and seamless go-to-market execution

Conduct in-depth market research and competitive analysis to identify current state, emerging trends, and opportunities for differentiation

Partner with industry partners, marketing, and sales teams to develop and execute effective go-to-market strategies, including positioning, messaging, and competitive differentiation

Define key metrics and establish robust reporting to monitor the performance of compute services, identify areas for improvement, and inform future decisions

Who You Are:

8+ years of professional experience, with 5+ years in Product Management of cloud services or in the AI/ML market segment

Deep understanding of cloud compute at the instance layer: how compute is provisioned, delivered, kept current, isolated, and reclaimed at scale

Proven ability to lead cross functional teams, with strong decision-making, project management, and mentoring skills

Demonstrated experience working closely with customers to identify needs and translate them into product features and solutions

Excellent verbal and written communication skills with the ability to influence cross-functional teams and stakeholders

Ability to think strategically while being able to dive into technical details to guide engineering teams

Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field. MBA or equivalent experience is a plus

Preferred:

Hands on experience with a large-scale compute or instance service, such as EC2, Google Compute Engine, Azure Compute, or an equivalent internal provisioning platform

Familiarity with accelerated compute, high-performance interconnect, and the behavior of large multi-node workloads

Good gras …

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