Senior ML Engineer, Recommender Systems (Gaming Discovery)

Epic Games; California; On-site; USD 209,000 - 307,000; Full time

Job summary

Epic Games' Data & Analytics teams design, build, and optimize the recommendation systems powering Fortnite Discover, delivering personalized experiences across a vast catalog of creator content. You will work across the full stack—from candidate generation to real-time reranking—in a high-scale, fast-moving environment.

The role emphasizes experimentation, ML infrastructure decisions, and collaboration with analytics and content teams, with opportunities to impact ranking signals, content

Qualifications

• 5+ years of experience building production recommendation or ranking systems.
• Experience with deep learning for information retrieval and multi-stage pipelines.
• Strong Python engineering skills with PyTorch and Spark.
• Ability to design and analyze A/B experiments and interpret biases.

Responsibilities

• Design and implement retrieval, ranking, and reranking models for creator content using deep learning approaches.
• Build multi-stage candidate generation and impression allocation pipelines for relevance, diversity, and fair content exposure.
• Design and run A/B experiments and establish evaluation frameworks for holistic recommendation quality.
• Collaborate with analytics and content quality teams on ranking signals like genre classification and content quality metrics.
• Own ML infrastructure decisions, balancing batch, near-real-time, and streaming serving architectures.

Skills

Production ranking systems

Deep learning for IR

A/B testing

Python engineering

PyTorch framework

Cloud ML environments

Tools

PyTorch

Spark

Databricks

AWS

Ray

Transformers

TorchRec

Job description

Epic Games' Data & Analytics teams design, build, and optimize the recommendation systems powering Fortnite Discover, delivering personalized experiences across a vast catalog of creator content. You will work across the full stack—from candidate generation to real-time reranking—in a high-scale, fast-moving environment.

The role emphasizes experimentation, ML infrastructure decisions, and collaboration with analytics and content teams, with opportunities to impact ranking signals, content