The posting, in Epic Games's own words
archived Oct 21, 2026WHAT MAKES US EPIC?
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. 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.
What We Do
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.
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What You'll Do
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. You'll work across the full recommendation stack: candidate generation, content ranking, impression allocation, and real-time reranking. 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.
In this role, you will
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 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 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 Collaborate with analytics and content quality teams on ranking signals including genre classification, creator credibility, and content quality metrics Own ML infrastructure decisions: choosing the right tradeoffs between batch, near-real-time, and streaming serving architectures