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Senior Staff Machine Learning Engineer in Relevance to define and lead the technical direction for Patreon’s recommendation and search systems. Architect large-scale ML systems that determine what fans see, how they discover creators, and how creators grow their audiences. Own projects from prototype to production, including the data, modeling, and backend components.
Job Responsibility:
Design and build large-scale ranking, search, and recommendation systems that power discovery across the platform
Architect and productionize end-to-end ML pipelines (data ingestion, feature generation, model training, evaluation, deployment, and monitoring)
Prototype visualizations for the impact of new ML models to help bridge collaboration with product and design partners
Develop embedding systems and retrieval models that serve millions of users in real time
Establish and evolve ML observability and performance monitoring for relevance metrics
Partner with cross-functional leaders to align on roadmap, goals, and impact
Mentor and guide engineers, fostering a high bar for technical excellence and experimentation
Requirements:
8+ years of professional experience in applied machine learning, with a focus on search, ranking, or recommendation systems
Proven experience designing and deploying production ML systems serving millions of users
Strong programming skills in Python
Proficiency in backend development and experience integrating ML systems into production environments
Ability to prototype or visualize ML outputs in interactive UI contexts to help teams interpret model impact
Expertise in building and maintaining data pipelines, feature stores, and model observability systems
Deep understanding of embedding-based retrieval, ranking algorithms, and personalization architectures
Strong collaboration skills — able to partner effectively with engineers, designers, and PMs
Experience mentoring senior engineers and driving cross-team technical alignment
Master’s or PhD in Computer Science, Machine Learning, or related field, or equivalent experience
Nice to have:
Experience at a large-scale consumer platform with search or recommendation systems
Prior experience building internal tools or interactive demos for model interpretability
Strong interest in creative ecosystems or content discovery platforms