Senior Software Engineer, ML Verticals - 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><h3><strong>What You’ll Do:</strong></h3> <p>Reporting to the Engineering Manager for ML Verticals, you will help design and build new mechanisms in WB that allow machine learning practitioners to analyze application-specific data across domains such as robotics, finance, and health/bio.</p> <p>The ML Verticals team focuses on rapidly exploring and shipping new analysis workflows, often in problem spaces where the right abstractions, data models, and UX are still emerging. You’ll work closely with users to understand how they want to interrogate their data, then iterate on product experiences by shaping frontend interactions <em>and</em> the underlying data representations that make them possible.</p> <p>This role is frontend-led, but explicitly end-to-end: you’ll move quickly across UX, APIs, and data models to turn early ideas into production features used by thousands of customers.</p> <p> </p> <p><strong>About the role:</strong></p> <ul> <li>Rapidly prototype, iterate, and ship new analysis workflows for ML verticals, balancing experimentation with long-term product quality</li> <li>Design frontend experiences in parallel with backend ... (truncated, view full listing at source)