Senior Software Engineer, ML Verticals - Weights & Biases

Weights & Biases•Sunnyvale, CA

4d•$139,000 - $204,000•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. Reporting to the Engineering Manager for ML Verticals, you will help design and build new mechanisms in W&B that allow machine learning practitioners to analyze application-specific data across domains such as robotics, finance, and health/bio. 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 and the underlying data representations that make them possible. 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.

Requirements

3–5+ years of experience building user-facing software, with a strong focus on frontend development
Comfortable working across the stack, including backend services, APIs, and data models when product needs require it
Experience working in ambiguous problem spaces where requirements, schemas, and abstractions evolve over time
Strong product instincts and ability to reason from user goals to technical design decisions
Familiarity with modern web technologies such as React, TypeScript, and GraphQL
Solid understanding of programming fundamentals and experience with at least one backend or systems language (e.g. Go, Python, Java, C++, JavaScript)
Bias toward action, iteration, and learning through building, with the judgment to avoid over-engineering too early

Nice To Haves

Experience evolving or versioning data schemas in production systems
Experience designing APIs to support exploratory or analytical workflows
Familiarity with large-scale data systems, time-series data, media, or domain-specific file formats
Exposure to ML, robotics, simulation, or scientific tooling

Responsibilities

Rapidly prototype, iterate, and ship new analysis workflows for ML verticals, balancing experimentation with long-term product quality
Design frontend experiences in parallel with backend schemas and APIs, evolving data models as user needs become clearer
Work across the stack to implement end-to-end features, including adding or modifying logging formats, APIs, and data access patterns
Make pragmatic decisions about how user-logged data should be stored, queried, and rendered to support new analytical questions
Collaborate closely with product, design, and customer-facing teams to validate ideas early and incorporate real user feedback
Improve reliability, performance, and scalability when working with large, complex, or high-volume datasets
Contribute clean, testable, and reusable code; participate in code reviews and help establish patterns that reduce one-off solutions