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.
You’ll join the ML Workflows organization as the Engineering Manager for Artifacts & Registry, leading a team of six engineers.
Registry is the organization-level source of truth for what a company has approved for production. It provides access control, protected aliases, lineage, and automation hooks, and it is where a model version goes once it moves out of research. Pinterest, Capital One, Canva, Recursion, and Dropbox all run on it, and for some of them it sits inside a production serving path. Artifacts is the layer underneath it: content-addressable storage, deduplication, lineage, and retention for datasets, model weights, checkpoints, and evaluation outputs, across S3, GCS
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