Senior Machine Learning Engineer, Relevance

Patreon•New York, NY

1d•Hybrid

About The Position

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 to support our mission. This role is based in San Francisco or New York as an in-office 2 days per week on a hybrid work model. 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, ranking, feed relevance, 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.

Requirements

Experience working in an end-to-end machine learning team environment (typically 5+ years): analyzing data, building and iterating on machine learning models, writing production-level code and shipping to production, monitoring performance, and A/B testing.
Writes clean and robust code in Python or other programming languages, and provides substantive, constructive feedback in code reviews.
Experience debugging complex systems with a systematic approach.
Analyzes datasets thoroughly to develop product and customer insights.
Writes clear technical documentation for both technical and non-technical audiences.
Seeks and incorporates feedback on code, models, and technical approaches.
Bachelor's degree in Computer Science, Computer Engineering, or a related field, or the equivalent

Responsibilities

Conduct exploratory data analyses and proof-of-concept machine learning models to understand opportunities and potential project impact.
Collaborate with cross-functional partners, such as product, engineering, design, legal, and trust and safety to design effective machine learning solutions.
Analyze and prepare training data, including using crowdsourcing data labeling techniques.
Train and iterate on machine learning models using novel techniques.
Deploy machine learning models to production and write backend code when necessary to properly deploy the model.
Debug models when observability shows performance gaps, and iterate on models.