Software Engineer, Metrics and Storage- Weights & Biases

<div class="content-intro"><div id="message-list_1758129707.765969" class="c-virtual_list__item" data-qa="virtual-list-item" data-item-key="1758129707.765969"> <div class="c-message_kit__background c-message_kit__background--hovered p-message_pane_message__message c-message_kit__message" data-qa="message_container" data-qa-unprocessed="false" data-qa-placeholder="false"> <div class="c-message_kit__hover c-message_kit__hover--hovered" data-qa-hover="true"> <div class="c-message_kit__actions c-message_kit__actions--default"> <div class="c-message_kit__gutter"> <div class="c-message_kit__gutter__right" data-qa="message_content"> <div class="c-message_kit__blocks c-message_kit__blocks--rich_text"> <div class="c-message__message_blocks c-message__message_blocks--rich_text" data-qa="message-text"> <div class="p-block_kit_renderer" data-qa="block-kit-renderer"> <div class="p-block_kit_renderer__block_wrapper p-block_kit_renderer__block_wrapper--first"> <div class="p-rich_text_block"> <div class="p-rich_text_section">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.</div> <div class="p-rich_text_section"><br>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.</div> <div class="p-rich_text_section"><br>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.</div> <div class="p-rich_text_section"><br>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.</div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div></div><p><strong>What You’ll Do</strong><strong><br></strong> The <strong>Metrics and Storage team</strong> builds and scales the infrastructure behind Weights Biases’ flagship Models product, which powers experiment tracking and analytics for over 2,500 of the world’s leading AI and ML teams. The team focuses on data ingestion, storage, and querying at a petabyte scale, optimizing for performance, reliability, and cost-effectiveness.</p> <p><strong>About the role</strong><strong><br></strong>As a Software<strong> </strong>Engineer, you’ll lead efforts to scale our metrics and storage systems to meet the needs of our most advanced customers. Day to day, you’ll set technical direction, balancing near-term deliverables with long-term architectural improvements. You’ll partner with product, revenue, and engineering teams across WB to evolve our platform and ensure it continues to meet customer demands. You’ll also help mentor engineers and drive best practices across the organization.</p> <p><strong>Who You Are</strong></p> <ul> <li>4+ years of experience in software engineering, with a focus on data platforms and/or distributed systems<br><br></l ... (truncated, view full listing at source)