Research Scientist (Visual Generative AI & World Models)
As a Research Scientist at Graphcore, you will advance AI research at the intersection of visual generative modeling, multimodal learning, world models, and hardware-aware machine learning. You will explore new model architectures, training methods, and deployment strategies with applications in embodied AI, robotics, and autonomous systems. This role sits at the interface between frontier model research and AI hardware, contributing to the development of innovative AI models and influencing the future of AI compute.
Key responsibilities include developing and evaluating new ideas in visual generative AI, multimodal modeling, and world models, from initial hypothesis through experiment design, implementation, analysis, and publication. You will prepare, submit, and present your work to AI conferences and workshops. Additionally, you will collaborate with researchers, software engineers, and silicon teams to understand how emerging AI workloads can shape, and be shaped by, future Graphcore hardware and software systems.
The ideal candidate will have a Master's, PhD, or equivalent experience in a technical discipline such as Mathematics, Statistics, Computer Science, Physics, Chemistry, or Biomedical Engineering. Experience in visual generative AI, visual understanding, or world models is essential. Strong Python programming skills using a modern deep learning framework, such as PyTorch or JAX, are required. Familiarity with deep learning fundamentals, including model architectures, optimization, evaluation, and scaling, is necessary. The candidate should have the ability to design, execute, analyze, and clearly communicate machine learning experiments, supported by a solid foundation in calculus, probability theory, and linear algebra. Evidence of research ability, such as conference or workshop submissions, publications, technical reports, open-source projects, or impactful industrial research, is expected.
Graphcore offers a collaborative and supportive work environment where researchers organize around individual interests and solve problems together. The team spans multiple locations, including London, Cambridge, and Bristol, and engages in projects involving efficient compute, model scaling, distributed training and inference, and AI models for various modalities and applications. This role provides an opportunity to work at the cutting edge of AI and help shape the hardware and software systems that drive the future of AI compute.