Senior Machine Learning Engineer, Discovery Recommendations

WHAT 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. ANALYTICS 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. 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 What we're looking for - 5+ years of experience building production recommendation or ranking systems, ideally in a UGC, marketplace, or content discovery context - Experience with deep learning for information retrieval and multi-stage recommendation pipelines (candidate generation, scoring, reranking) - Demonstrated ability to design and analyze A/B experiments, with awareness of biases inherent to recommendation systems - Strong Python engineering skills with experience in PyTorch and large-scale data processing frameworks (Spark preferred) - Comfort working in a cloud-based ML environment - Experience with explore/exploit strategies, content cold-start, or counterfactual evaluation methods applied to recommendation - Experience with content understanding models (NLP, vision, or generative AI) used as ranking features - Familiarity with creator economy dynamics and how recommendation design affects content quality and creator incentives - Experience with our stack: PyTorch (TorchRec, Transformers), Ray, Databricks, AWS - Passion for video games and/or experience with gaming analytics This role is open to multiple locations across the US (including CA, NYC, & WA). EPIC JOB + EPIC BENEFITS = EPIC LIFE Our intent is to cover all things that are medically necessary and improve the quality of life. We pay 100% of the premiums for both you and your dependents. Our coverage includes Medical, Dental, a Vision HRA, Long Term Disability, Life Insurance & a 401k with competitive match. We also offer a robust mental well-being program through Modern Health, which provides free therapy and coaching for employees & dependents. Throughout the year we celebrate our employees with events and company-wide paid breaks. We offer unlimited PTO and sick time and recognize individuals for 7 years of employment with a paid sabbatical. Pay Transparency Information The expected annual base pay range(s) for this position are detailed below. Each base pay range is relevant only for individuals who are residents of or will be expected to work within the specified locale. Compensation varies based on a variety of factors, which include (but aren’t limited to) things such as skills and competencies, qualifications, knowledge, and experience. In addition to base pay, most employees are eligible to participate in Epic’s generous benefit plans and discretionary incentive programs (subject to the terms of those plans or programs). New York City Base Pay Range $232,666—$341,243 USD California Base Pay Range $209,399—$307,119 USD Washington Base Pay Range $190,363—$279,199 USD AB

5+ years of experience building production recommendation or ranking systems, ideally in a UGC, marketplace, or content discovery context. Experience with deep learning for information retrieval and multi-stage recommendation pipelines (candidate generation, scoring, reranking). Demonstrated ability to design and analyze A/B experiments, with awareness of biases inherent to recommendation systems. Strong Python engineering skills with experience in PyTorch and large-scale data processing frameworks (Spark preferred). Comfort working in a cloud-based ML environment. Experience with explore/exploit strategies, content cold-start, or counterfactual evaluation methods applied to recommendation. Experience with content understanding models (NLP, vision, or generative AI) used as ranking features. Familiarity with creator economy dynamics and how recommendation design affects content quality and creator incentives. Experience with our stack: PyTorch (TorchRec, Transformers), Ray, Databricks, AWS. Passion for video games and/or experience with gaming analytics.

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.