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Machine Learning Engineer

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Onsite

Machine Learning Engineer

Epic Games

Machine Learning Engineer

Skills

PythonNumPyPyTorchMachine Learning

2 months ago

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Important information


Location:

London, UK

Work mode:

Onsite

Published on:

17 August 2026

What they need


Who you are

  • PhD in Computer Science, Mathematics or related field, or 3+ years of relevant industry experience
  • Experience creating machine learning algorithms for vision and/or language problems and deploying them as production-level systems
  • Expert, hands-on knowledge of:
  • Foundational models and vision and/or language, incl. local deployment and model post-training of open weights models
  • Memory, state, or long-horizon behaviour in foundation models: your experience may come from language agents and long-context methods, reinforcement learning under partial observability, world models, or sequence modelling
  • Python and associated ML tools/frameworks (numpy, scipy, sklearn, pytorch)
  • Experience with working collaboratively with other developers, following a flexible and agile approach

What the job involves

  • 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
  • As foundation models move from answering questions to acting over long horizons, memory becomes a central requirement. Systems must decide what is worth keeping and improve from experience. Large context windows and retrieval systems are not enough. You will work with researchers on projects across the company that require memory in different modalities to define general solutions that enable models to represent experience, decide what they keep and what they discard, and to efficiently store, retrieve and exploit memory
  • Design new memory architectures that work across problems, modalities and products
  • Define new ways to measure and evaluate memory performance
  • Help create and deliver production-ready, scalable and high-quality learning models and associated algorithms
  • Critically assess the effectiveness of such models and make recommendations for the ongoing roadmap

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