Research Intern, PhD or Master's (Contract)

Discovered

Riot Games

SingaporeexternalData and AnalyticsPosted about 2 months ago (Jun 26, 2026)

About the role

At the Riot Singapore Efficiency team, we're pushing the frontiers of how Generative AI can power the next generation of creativity and game development. We leverage cutting-edge machine learning approaches to reimagine how art, animation, and design content are produced and enhanced across our games. We are seeking a passionate and skilled intern (PhD or Master's level) to join our R&D efforts. As a

Generative AI Research

Intern, you will explore foundational or applied challenges in generative modeling and contribute to building state-of-the-art systems that empower creatives. Please note that Riot Games does not offer relocation packages for this position. You need to be able to work legally in Singapore, without Riot Games’ sponsorship, to be considered.

Responsibilities

• Investigate and prototype advanced generative models targeting creative pipelines. • Collaborate with interdisciplinary stakeholders to ensure research aligns with real-world constraints and goals. • Share results through internal demos, documentation, or technical writeups.

Required Skills

• Currently pursuing a Master’s or Ph.D. in Computer Science, Machine Learning, Statistics, or a related field. • Strong theoretical background in machine learning and generative modeling. • Hands-on experience with PyTorch, TensorFlow, or JAX. • Familiarity with models such as diffusion and score-based generative models, flow matching, GANs, VAEs, and Transformers. • Excellent communication and documentation skills.

Desired Skills

• Experience with multimodal modeling, controllable generation, or diffusion-based frameworks. • Interest in games, digital creativity tools, or player-facing applications. • Publications or work in submission at top ML/AI/CV/CG conferences (NeurIPS, ICLR, CVPR, ICML, SIGGRAPH).

Our Perks

• Mentorship from senior researchers and engineers. • Access to internal tools, datasets and compute for experimentation.

Not published in this posting

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