AI/ML Engineer

Epic Games
BLANK,BLANK,Multiple LocationsPosted 20 March 2026

Job 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’ll join the Advanced Analytics ML Services team to design, build, and operate scalable ML systems end-to-end from RD workflows through production serving and monitoring. This role is hands-on programming with real ownership where you’ll ship foundational infrastructure, enable data scientists, and push new capabilities like agentic tooling that accelerates how we build and maintain analytics/ML systems. You’ll work across domains like recommendations and ranking, bandit-based optimization, graph analytics, and player behavior modeling at blazingly fast big-data scale. In this role, you will Build production ML systems: Design, implement, and maintain scalable solutions for batch and real-time ML workloads, ensuring models and their outputs are served reliably Own ML lifecycle infrastructure: Experiment tracking, model registry, job orchestration, alerting Ship AI Agentic Tooling: Develop and implement reusable AI tooling, skills, and plugins to streamline ML and analytics workflows to empower Data Scientists and other programming teams Partner closely with Analytics, Programming, and Product: Prioritize solutions and ensure incremental delivery that drives the most impact What we're looking for Minimum 3+ years of Machine Learning Programming experience in a data-driven environment, designing and implementing production analytic/data systems, ideally in a AAA video gaming or streaming entertainment context Skilled at working with AI tooling (ChatGPT, Cursor, Claude Code, etc) to assist in the development process Strong technical background with a primary focus on programming and MLOps practices over pure Data Science research Fluent with modern Data/ML stack: Orchestration (Airflow), Big Data Pipelines (Databricks), Model Registry and Experiment Tracking (MLFlow) Strong development practices using python and Docker for portable and extensible tooling Experience building infrastructure for recommendation engines and real time optimization algorithms Thrives in Ambiguity and action oriented. Can take fuzzy goals and build out prototypes and iterate quickly to output workable deliverables incrementally Passion for video games and/or video game technology This role is open to multiple locations in North America (including CA, NYC, WA). 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 $140,696 $206,353 USD California Base Pay Range $123,812 $181,591 USD Washington Base Pay Range $132,254 $193,972 USD 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 c ... (truncated, view full listing at source)
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