Role overview
- Prototype and evaluate novel generative systems for game content, including 2D/3D assets, characters, environments, and narrative systems.
- Design scalable model training pipelines tailored to interactive and real-time applications.
- Bridge the gap between research and production by deploying AI systems into live or in-development game projects.
- Collaborate closely with game designers, artists, and engineers to build AI tools that enhance creativity and production efficiency.
- Explore new architectures and methods for controllable and style-consistent generative models in games.
Responsibilities
- Design and implement generative AI systems tailored to game production workflows.
- Train, fine-tune, and evaluate large-scale ML models for asset creation, gameplay systems, or player interaction modeling.
- Conduct large-scale experiments to measure model quality, player impact, and production efficiency gains.
- Develop internal AI tools that assist artists, designers, and developers.
- Collaborate cross-functionally to ensure research outcomes translate into measurable product improvements.
- Contribute technical documentation, research publications, or knowledge-sharing initiatives.
Basic qualifications
- PhD in Computer Science, Artificial Intelligence, Machine Learning, Computer Graphics, or a related field, or equivalent practical experience in applied research.
- Experience in one or more of the following areas: generative models, computer vision, machine learning, large language models, diffusion models, or multimodal AI.
- Strong programming skills in Python and experience building research prototypes.
- Track record of research contributions through publications, open-source projects, technical blogs, or conference submissions (e.g., NeurIPS, ICML, ICLR, CVPR, SIGGRAPH, etc.).
Preferred qualifications
- 2+ years of hands-on coding experience in ML or AI-related projects.
- Experience leading or initiating research directions or independent projects.
- Experience with PyTorch, JAX, or similar deep learning frameworks.
- Practical experience training, fine-tuning, or deploying large-scale models (e.g., LLMs, diffusion models, multimodal systems).
- Strong quantitative analysis skills and experience working with gameplay data or user interaction data.
- Interest in game development, interactive systems, or procedural content generation.
Benefits
- Flexitime
- Work from home
- United Kingdom (preferred)
Tags & focus areas
Used for matching and alerts on DevFound Fulltime Remote Ai Machine Learning Deep Learning Computer Vision Pytorch Generative Ai