Memory Architect for Foundation Models

Epic Games

Greater London

On-site

GBP 90,000 - 130,000

Full time

14 days+

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Job summary

Epic Games is seeking an engineer for the GAMES division to advance memory-focused AI models and memory-management techniques across projects. You will collaborate with researchers and engineers to design architectures and evaluate memory performance, producing production-ready learning models and algorithms.

The role emphasizes cross-disciplinary collaboration, rigorous evaluation, and contribution to the company’s roadmap for advanced AI capabilities in Unreal Engine and related products.

Qualifications

  • PhD in Computer Science, Mathematics or related field or 3+ years of industry experience.
  • Experience creating machine learning algorithms for vision and/or language problems.
  • Expert, hands-on knowledge of foundational models and memory in AI systems.
  • Proficiency with Python and ML tools such as PyTorch, NumPy, SciPy, scikit-learn.

Responsibilities

  • 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.

Education

PhD in Computer Science, Mathematics or related field
3+ years of relevant industry experience

Tools

PyTorch
NumPy
SciPy
scikit-learn

Job description

Epic Games is seeking an engineer for the GAMES division to advance memory-focused AI models and memory-management techniques across projects. You will collaborate with researchers and engineers to design architectures and evaluate memory performance, producing production-ready learning models and algorithms.

The role emphasizes cross-disciplinary collaboration, rigorous evaluation, and contribution to the company’s roadmap for advanced AI capabilities in Unreal Engine and related products.

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