Job Description
CoreWeave, the AI Hyperscaler™, acquired Weights & Biases to create the most powerful end-to-end platform to develop, deploy, and iterate AI faster. Since 2017, CoreWeave has operated a growing footprint of data centers covering every region of the US and across Europe, and was ranked as one of the TIME100 most influential companies of 2024. By bringing together CoreWeave’s industry-leading cloud infrastructure with the best-in-class tools AI practitioners know and love from Weights & Biases, we’re setting a new standard for how AI is built, trained, and scaled. The integration of our teams and technologies is accelerating our shared mission: to empower developers with the tools and infrastructure they need to push the boundaries of what AI can do. From experiment tracking and model optimization to high-performance training clusters, agent building, and inference at scale, we’re combining forces to serve the full AI lifecycle — all in one seamless platform. Weights & Biases has long been trusted by over 1,500 organizations — including AstraZeneca, Canva, Cohere, OpenAI, Meta, Snowflake, Square,Toyota, and Wayve — to build better models, AI agents and applications. Now, as part of CoreWeave, that impact is amplified across a broader ecosystem of AI innovators, researchers, and enterprises. As we unite under one vision, we’re looking for bold thinkers and agile builders who are excited to shape the future of AI alongside us. If you're passionate about solving complex problems at the intersection of software, hardware, and AI, there's never been a more exciting time to join our team. What You'll Do You'll be joining the ML Workflows organization as the Engineering Manager for the Launch & Sandboxes team. Our team owns two interconnected product areas: Sandboxes, W&B's cloud execution environments that let ML practitioners run code in isolated, GPU-enabled containers directly from the W&B platform, and Launch, which handles job launching, compute orchestration, and integration with HPC schedulers. We build the infrastructure that connects W&B's experiment tracking and model development tools to the actual compute where ML work happens, spanning Kubernetes clusters, HPC nodes, and cloud VMs. 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 Famil