Senior Machine Learning Engineer, Infrastructure

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 fans and build a lasting business including: paid memberships, free memberships, community chats, live video, 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: - $10 billion+ generated by creators since Patreon's inception - 100 million+ free memberships for fans who may not be ready to pay just yet, and - 25 million+ paid memberships on Patreon today. We're continuing to invest heavily in building the best creator platform with the best team in the creator economy and are looking for a Senior Machine Learning Engineer, Infrastructure to support our mission. This role is based in San Francisco or New York as an in-office 3 days per week on a hybrid work model. About the Team You'll join the Relevance team, whose mission is to build the ML systems that power how fans discover creators and how content surfaces across Patreon. The team is responsible for search, feed ranking, and creator-fan matching. You'll work closely with a small, collaborative group of MLEs on shared infrastructure, code reviews, and roadmap alignment, while partnering cross-functionally with Product, Data Engineering, and Trust & Safety to deliver measurable impact across the platform. About the Role - Architect, scale, and maintain high-throughput, low-latency live inference infrastructure to support our relevance systems. - Own the end-to-end feature store lifecycle—from ingestion and transformation to production serving, ensuring high availability and consistency between online and offline features. - Design and implement observability, monitoring, and validation frameworks to detect performance gaps, latency spikes, and production drift. - Collaborate with cross-functional partners, such as product, data e