E
Epic Games
Machine Learning Internship
Skills
Deep LearningPythonC#NumPyPyTorchData AnalysisMachine LearningAINLP / Natural Language Processing
2 months ago
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Important information
Salary:
$111,223 – $166,834 / year
Location:
United Kingdom
Work mode:
Remote
Published on:
18 August 2026
What they need
Who you are
- An ongoing or recently completed (within the last 12 months) PhD in Computer Science, Mathematics, AI or a related field - recent MSc graduates in the same fields will also be considered
- Experience building models or algorithms in computer vision, natural language processing, or speech/audio/acoustic processing
- Hands-on knowledge of modern deep learning methods, such as Transformers, LLM fine-tuning, and diffusion models
- Strong coding skills in Python and the standard ML stack (PyTorch, NumPy, SciPy, scikit-learn)
- Desirable: Game dev experience in either C++ or C# (including personal projects)
- Internship Details
- Duration: 6-12 months, with flexible start dates throughout 2026/2027
- Hours: full-time (40 hours/week) or part-time (20 hours/week)
- Location: Select regions within the UK, US or Canada
- Employment Authorization: You must hold an existing right to work in the UK, US or Canada for the full duration of the internship - Visa sponsorship or support is unavailable for this role
What the job involves
- Epic Games is looking for current PhD students and recent PhD or MSc graduates (within 12 months of graduation) for paid, 6-12 month research internships with our Special Projects & Epic Research Group
- You will work with our team of research scientists and engineers at the intersection of vision, language, speech processing and game development to create machine learning models in a range of areas to support game developers and improve user experience
- Create machine learning models in areas including player safety, game agents (e.g. AI-backed characters, VLA agents), world/scene and game logic generation, developer tools, and improved user experience
- Work alongside our team of research scientists and engineers on applied research problems
- Own data analysis and data creation, supported by an internal team of labellers
- Design, implement, and experiment with models end to end
- Gain exposure to engineering work around deploying models at scale
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