Senior Technical Writer - Weights & Biases, Models

Weights & Biases•Sunnyvale, CA

4d•$143,000 - $210,000•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 Director of Product, as a Senior Technical Writer you will join the Docs Team to write documentation for W&B’s machine learning developer tools. You will partner closely with Product and Engineering to document product updates and try new functionality to build subject-matter expertise. Collaborate with Growth and Sales to drive updates on what features are most in demand for additional context and documentation.

Requirements

5+ years writing technical docs. You’ve written API and product documentation for software developers, data scientists, or machine learning practitioners as your primary job for at least a decade.
Scripting expertise. You’ll spend a lot of time in the code our repo libraries (examples: the wandb library and weave library), using our Python SDK functionality to understand how products work for different use cases. You’ll use your automation skills to generate documentation solutions, including API use to call LLMs or download data for transformation into docs.
ML experience: You’ve are familiar with training workflows and used common ML tools and frameworks such as PyTorch, Keras, Tensorflow, and Scikit-learn.

Nice To Haves

Experience using AI at work. Ideal candidates will have experience using generative AI to assist with documentation engineering, interfacing with the APIs of LLM providers, and optimizing their solution through testing.
Scrappy mindset: You’ll be moving quickly to get updates into the docs, ship small incremental improvements continuously, and quickly measure the results of changes.
Ownership mindset: Ability to own large-scoped projects that involve multiple stakeholders. Experience creating and documenting best practices for our team.
Outgoing and friendly: You'll love this role if you enjoy connecting with real users day to day, helping them solve issues and understand good patterns for using our tools. Day to day you'll be answering questions and requests with a kind, thoughtful tone that makes users feel appreciated and connected to our team.
Autonomous: If you work well in a self-directed environment, and proactively find ways to improve processes and collaborate with team members or engaged users, your initiative will really shine in this role.
Curious and driven: Explore machine learning and learn more about the engineering stack and common ML workflows. Solve problems in both fast-paced, short term sprints and in larger, more long-term projects.

Responsibilities

Prioritize and execute on docs projects. Roll out new product docs, tutorials and guides, example scripts, generated reference docs, and rework existing content to keep it fresh, relevant, and engaging.
Use the product. Test the user workflows you are documenting, and build a deeper understanding of how our tools fit into the machine learning lifecycle. Run example scripts, and collaborate on new scripts to be included in guides.
Regularly aggregate feedback. Capture perspectives from external users and internal team members. Collaborate with Support, Customer Success, Growth, and internal ML Practitioners.