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Engineering Manager - Relevance at Eventbrite, Inc.. Location Information: USA. Remote in US , Engineering Manager - Relevance. THE CHALLENGE. As the Engineering Manager for Relevance, you will lead Eventbrite's mission to make live events more discoverable and engaging for users globally. Your leadership and technical expertise will guide the development of innovative recommendation algorithms and user-focused features, transforming event discovery into a highly personalized, fresh, and context-aware experience across our website and mobile app. This role offers an exceptional opportunity to significantly impact the way people connect through live experiences in today's digital age, influencing millions of users and reinforcing Eventbrite’s position as the leading platform for live experiences. THE TEAM. The Relevance team at Eventbrite consists of talented engineers focused on building advanced systems that deliver personalized event recommendations to users around the globe. We leverage sophisticated machine learning techniques, including deep learning and Large Language Models (LLMs), to understand user interests, context, and event content, ensuring each interaction with our platform feels uniquely tailored and relevant. Our team thrives in a highly collaborative environment, working closely with product, data science, design, and analytics partners. We prioritize agility, continuous improvement, and innovation, engaging frequently in knowledge-sharing sessions, technical demos, and hackathons to push the boundaries of personalized event discovery.. THE ROLE. Provide strategic technical and people leadership to a full-stack team focused on event discovery, recommendations, and frontend integration across web and mobile platforms.. Develop and drive a technology roadmap emphasizing continuous improvement in event relevance via ML, personalization strategies, and emerging technologies like Large Language Models (LLMs) for event categorization, query understanding, and personalized content generation.. Foster an agile, delivery-focused, and high-performance engineering culture.. Collaborate closely with product, data science, design, and analytics teams to implement and measure impactful improvements in recommendation quality.. Mentor engineers actively, fostering accountability, collaboration, professional growth, and excellence.. Maintain clear, transparent communication with stakeholders about progress, challenges, and achievements.. Champion software engineering best practices, including rigorous testing, deployment automation, scalability, maintainability, and technical excellence.. THE SKILLSET. Experienced engineering manager (3+ years managing engineering teams) with a proven ability to build and guide highly effective teams in dynamic, high-growth environments.. Technical leader experienced in overseeing ML-driven and personalization-focused feature development, ideally within consumer-facing web or mobile applications.. Practical understanding of recommendation systems (e.g., Two-Tower architectures, ranking systems, contextual personalization) and proven experience with metrics-driven experimentation frameworks (A/B testing, analytics-driven decision-making, click-through rates, conversion rates, dwell time, and user retention).. Familiarity with applying Large Language Models (LLMs) to enhance recommendation systems, improve content understanding, or facilitate personalization.. Proven track record in systematically managing and modernizing legacy systems, improving architectures, and ensuring scalability.. Exceptional interpersonal and communication skills, including effective stakeholder management, conflict resolution, empathetic leadership, and the ability to maintain team alignment through growth and change.. Experienced in nurturing engineering talent, fostering a culture of curiosity, continuous learning, and high-quality delivery.. Skilled in partnering closely with product and design teams to deliver compelling data-driven user experiences.. BONUS POINTS. Experience with large-scale, consumer-facing event platforms, marketplaces, or recommendation-centric applications.. Knowledge of modern cloud architectures, microservices, and data-intensive development environments (AWS, Kafka, Elasticsearch, Kubernetes, etc.).. Direct experience improving team performance metrics such as velocity, reliability, and engineering satisfaction.