Staff Machine Learning Engineer, Taxonomy

Patreon is a media and community platform where over 300,000 creators give their biggest fans access to exclusive work and experiences. We offer creators a variety of ways to engage with their communities and build a lasting business including: paid memberships, free memberships, community chats, live experiences, and selling to fans directly with one-time purchases.

Ultimately our goal is simple: fund the creative class. And we're leaders in that space, with:

- $8 billion+ in revenue generated since Patreon's inception

- 60 million+ free new memberships for fans who may not be ready to pay just yet, and

- 10 million+ fans paying each month for exclusive access to creators' work and community.

As we scale the platform, understanding what our creators are making and what fans are consuming becomes increasingly important. We’re looking for a Staff Machine Learning Engineer to develop the content and creator classification systems that power discovery, recommendations, and platform insights.

This role is based in San Francisco or in New York and open to those who are able to be in-office 2 days per week on a hybrid work model.

About the Team

You'll join the Relevance team, whose mission is to use data to uncover key insights that drive Patreon's product strategy while instilling a culture of curiosity, scientific rigor, and data fluency across the organization. As the first dedicated Taxonomy MLE, you'll sit within the Relevance team — responsible for powering discovery, feed relevance, and creator-fan matching. You'll collaborate closely with the team on shared infrastructure, code reviews, and roadmap alignment, while partnering with Product, GTM, Trust & Safety, and other MLEs to ensure your classification systems serve the full breadth of Patreon's needs.

About the Role

- Build and deploy machine learning pipelines that generate taxonomies of creators, content, and risk profiles across the platform. These systems enable everything from improved r...