Senior Machine Learning Engineer, Discovery Recommendations

<div class="content-intro"><h2>WHAT MAKES US EPIC?</h2> <p>At the core of Epic’s success are talented, passionate people. Epic prides itself on creating a collaborative, welcoming, and creative environment. Whether it’s building award-winning games or crafting engine technology that enables others to make visually stunning interactive experiences, we’re always innovating.</p> <p>Being Epic means being a part of a team that continually strives to do right by our community and users. We’re constantly innovating to raise the bar of engine and game development.</p></div><h2>ANALYTICS</h2> <h3><strong>What We Do</strong></h3> <p>Our Data & Analytics teams build powerful stories and visuals that inform the games we make, the technology we develop, and business decisions that drive Epic.</p> <h3><strong>What You'll Do</strong></h3> <p>You will design, build, and optimize the recommendation systems that power Fortnite's Discover experience, serving personalized recommendations to one of the largest player bases in gaming across a massive catalog of creator-built experiences.</p> <p>You'll work across the full recommendation stack: candidate generation, content ranking, impression allocation, and real-time reranking.</p> <p>Unlike recommendation systems that operate over a stable catalog, you're working with a massive, rapidly changing content library where new experiences are published daily, quality signals are sparse, and the system's own outputs shape the data it learns from.</p> <h3><strong>In this role, you will</strong></h3> <ul> <li>Design and implement retrieval, ranking, and reranking models for creator content using deep learning approaches (two-tower architectures, transformer-based sequence models, embedding-based retrieval) and build the user representation systems that power personalized discovery</li> <li>Build and optimize multi-stage candidate generation and impression allocation pipelines that balance relevance, diversity, and fair content exposure across a large and rapidly evolving catalog</li> <li>Design and run A/B experiments to validate model improvements, own evaluation frameworks that capture recommendation quality holistically, and drive the path from experiment to production deployment</li> <li>Collaborate with analytics and content quality teams on ranking signals including genre classification, creator credibility, and content quality metrics</li> <li>Own ML infrastructure decisions: choosing the right tradeoffs between batch, near-real-time, and streaming serving architectures</li> </ul> <h3><strong>What we're looking for</strong></h3> <ul> <li>5+ years of experience building production recommendatio