Machine Learning Engineer

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.

ENGINEERING - GAMES

What We Do

Unreal projects have been leading the pack of real-time entertainment with our constantly growing team of engineering experts. We’re always improving on the tools and technology that empower content developers worldwide.

What You'll Do

You will work with our team of applied research engineers at the intersection of computer vision,  gameplay agents, natural language processing, and machine learning to create state-of-the-art tools to support game developers in our ecosystem with a focus on bots (trained gameplay agents, intelligent NPCs, companions, world modeling), as well as automation of a number key areas (including moderation, security, and QA testing). You will be responsible for the design, implementation and deployment of production-ready machine learning models that can be integrated in our ecosystem.

In this role, you will

• Create and deliver production-ready, scalable and high-quality machine learning models and associated algorithms that can make actions in game environments
• Critically assess the effectiveness of such models and make recommendations for the ongoing roadmap
• Methodically understand and assess the effectiveness of new data channels and support collection/creation of new ones

What we're looking for

• • PhD in Computer Science, Mathematics or related field, or 3+ years of relevant industry experience 
  • Experience creating / applying machine learning algorithms for action modeling of bots behavior, vision-based or language-instructed, and deploying them as production-level systems in-game
  • Expert, hands-on knowledge of:
    • Deep learning, incl. but not limited to applications in the gaming industry
    • Foundational models and vision and/or language, especially  vision-language-action models, reinforcement learning agents, incl. local deployment and model post-training of open weights models
    • Reinforcement learning, incl. experience with agentic systems, reward design, policy optimisation, evaluation, and building scalable training environments for agents