> Markdown version of [/jobs/ext/4313015-machine-learning-engineer-discovery-recommendations](https://www.wearedevelopers.com/jobs/ext/4313015-machine-learning-engineer-discovery-recommendations). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Machine Learning Engineer, Discovery Recommendations

- **Company:** Epic Games, Inc.
- **Location:** Cary, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Big Data, Encodings, Video Game Development, Information Retrieval, Python (Programming Language), Recommender Systems, Software Deployment, Pytorch, Apache Spark, Deep Learning, Machine Learning Operations, Databricks
- **Published:** October 23, 2026
- **Apply:** https://startup.jobs/senior-machine-learning-engineer-discovery-recommendations-epic-games-10545519

## About the Role

* 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

## Description

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., * 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