Machine Learning Engineer, Discovery Recommendations at Epic Games

Cary, North Carolina, United States -

Full Time

Start Date

Immediate

Expiry Date

17 Jul, 26

Salary

0.0

Posted On

18 Apr, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine learning, Recommendation systems, Python, PyTorch, Spark, Deep learning, Information retrieval, Ranking models, A/B testing, Cloud computing, Data processing, NLP, Computer vision, Generative AI, AWS, Ray

Industry

Computer Games

Description

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 3-5+ 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 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. ABOUT US Epic Games spans across 25 countries with 46 studios and 4,500+ employees globally. For over 25 years, we've been making award-winning games and engine technology that empowers others to make visually stunning games and 3D content that bring environments to life like never before. Epic's award-winning Unreal Engine technology not only provides game developers the ability to build high-fidelity, interactive experiences for PC, console, mobile, and VR, it is also a tool being embraced by content creators across a variety of industries such as media and entertainment, automotive, and architectural design. As we continue to build our Engine technology and develop remarkable games, we strive to build teams of world-class talent. Like what you hear? Come be a part of something Epic! Epic Games deeply values diverse teams and an inclusive work culture, and we are proud to be an Equal Opportunity employer. Learn more about our Equal Employment Opportunity (EEO) Policy here. Note to Recruitment Agencies: Epic does not accept any unsolicited resumes or approaches from any unauthorized third party (including recruitment or placement agencies) (i.e., a third party with whom we do not have a negotiated and validly executed agreement). We will not pay any fees to any unauthorized third party. Further details on these matters can be found here.

Responsibilities

You will design, build, and optimize recommendation systems for Fortnite's Discover experience, managing the full stack from candidate generation to real-time reranking. Additionally, you will conduct A/B experiments to validate model improvements and collaborate with cross-functional teams to define ranking signals.