Senior Software Engineer, Workspaces- Weights & Biases

Weights & Biases•Sunnyvale, CA

5d•Hybrid

About The Position

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: The Models team builds and maintains the core user experience for tracking, visualizing, and analyzing machine learning experiments at Weights & Biases. Our full-stack engineering team works closely with Product, Design, and Platform partners to deliver high-impact, customer-facing features. We balance strong foundations in performance and reliability with a focus on building intuitive tooling that supports ML workflows at scale for both individual researchers and large ML organizations. About the role: As a Senior Software Engineer on the Models team, you will build core features that directly improve how ML engineers interact with and understand their experiment data. You will solve complex technical challenges across the stack to unlock faster, more reliable, and more intuitive user experiences. Working closely with Product and Design, you will translate nuanced user needs into high-quality, user-facing implementations. This role emphasizes technical ownership, iterative delivery, and raising the bar for performance and reliability at scale.

Requirements

5+ years of professional software engineering experience building and operating production web applications.
Demonstrated experience owning and delivering complex, user-facing features end to end, from technical design through release.
Strong experience with frontend development, including building complex user interfaces using React and TypeScript.
Strong experience with backend development, including designing, implementing, and maintaining backend services.
Proven ability to solve complex technical problems that impact performance, reliability, or usability in large-scale applications.
Experience working with APIs and data-fetching layers, with an understanding of how backend design affects frontend performance and user experience.
Ability to independently drive technical decisions and break down ambiguous problems into iterative, deliverable work.
Demonstrated technical leadership through influencing architecture, establishing or evolving technical patterns, and raising engineering best practices within a team or across the organization.

Nice To Haves

Experience working on data-intensive or analytics-heavy web applications.
Experience with GraphQL APIs, including query optimization and performance tuning.
Experience with Go or another compiled backend language.
Experience improving frontend performance for complex, highly interactive user interfaces.
Familiarity with tools, workflows, or products used by ML engineers, data scientists, or research teams.
Experience contributing to or evolving shared technical patterns and best practices across multiple teams.

Responsibilities

build core features that directly improve how ML engineers interact with and understand their experiment data
solve complex technical challenges across the stack to unlock faster, more reliable, and more intuitive user experiences
translate nuanced user needs into high-quality, user-facing implementations
technical ownership
iterative delivery
raising the bar for performance and reliability at scale