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CoreWeave

Engineering Manager, Launch & Sandboxes

Livingston, NJ / New York, NY / San Francisco, CA / Sunnyvale, CA / Bellevue, WA ; Bellevue, WA; Livingston, NJ; Manhattan, NY; San Francisco, CA; Sunnyvale, CA; US - RemoteRemote · United States
Manager · Technology
POSTED 26 MARVERIFIED 51M AGO
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Pay · H-1B filings
$320k
median · n=3 · FY26
Open for
221d
since 26 Mar
H-1B · FY26
64
UK sponsor
Hiring · 30d
305
+4 net · open roles
PythonGoKubernetesDockerLLMsDistributed systems

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

You’ll join CoreWeave’s IaaS organization as the Engineering Manager for the Launch & Sandboxes team. The team owns two connected areas: Sandboxes, cloud execution environments that let practitioners run code in isolated, GPU-enabled containers, and Launch, the services and integrations that support job submission, compute orchestration, and HPC schedulers.

You’ll lead the engineering work that connects developer workflows to the compute where AI workloads run. The systems span Kubernetes clusters, HPC nodes, and cloud virtual machines, and are designed to make it easier for customers to launch, manage, and scale workloads across CoreWeave’s infrastructure.

About the role

As the Engineering Manager for Launch & Sandboxes, you'll be involved in the full product lifecycle, from ideation and roadmap shaping through delivery and being available for customers. Your day-to-day will involve partnering with product management to prioritize across competing initiatives, unblocking your engineers on cross-team dependencies, and ensuring the team ships reliably while managing technical debt. You'll need to be technically fluent enough to participate in architecture discussions around container orchestration, distributed authentication, and billing infrastructure, and you're not afraid to get your hands dirty when the team needs it.

At the same time, your primary focus is growing your engineers into autonomous leaders and technical decision-makers. We're an AI-forward organization that embraces LLM-based coding tools and agentic workflows to accelerate development, and we'd expect you to champion that culture within your team. This role carries a heavy customer and user focus: you'll stay close to how ML practitioners use the product and make sure that perspective drives your team's priorities.

Like any role at a hyperscaler, this comes with high accountability and ownership. You'll own your team's outcomes end-to-end, from roadmap commitments through production reliability. We expect engineering managers to make hard prioritization calls, hold the bar on engineering quality, and build teams that operate with autonomy and urgency.

Who You Are

3+ years of engineering management experience, with a track record of shipping infrastructure or platform products

5+ years of software engineering experience prior to management, with backend systems depth

Technical fluency in Go, Python, and Kubernetes (you won't write code daily, but you need to review PRs, unblock architectural decisions, and understand production incidents)

Experience managing teams that own distributed systems in production

Demonstrated ability to balance product delivery with technical health (managing tech debt, reliability, on-call)

Experience partnering with product managers to shape and prioritize a roadmap

Track record of growing engineers across levels

Strong communication skills with both engineering and non-engineering stakeholders

Comfortable operating in ambiguity, especially in newer product areas where scope is still being defined

Preferred

Experience with ML infrastructure, ML platforms, or developer tools for data scientists

Familiarity with compute orchestration, job scheduling, or container isolation technologies

Experience managing teams through a product area transition (e.g., early-stage to growth, or post-acquisition integration)

Background in HPC or scientific computing environments

Experience with billing systems, metering, or usage-based pricing infrastructure

Source · CoreWeaveFirst seen 2 NovOriginal ↗