Research Scientist (Visual Generative AI & World Models)

Join Graphcore, a leading AI compute company, as a Research Scientist specializing in visual generative AI and world models. You will advance AI research, explore new model architectures, and collaborate with researchers and engineers. Enjoy a supportive work environment, flexible working arrangements, and opportunities for career progression.

• Develop and evaluate new ideas in visual generative AI, multimodal modelling and world models, from initial hypothesis through experiment design, implementation, analysis and publication.
• Prepare, submit and present your work to AI conferences and workshops.
• Work 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.
• Ability to design, execute, analyse and clearly communicate ML experiments
• Master’s, PhD or equivalent experience in a technical discipline (e.g., Mathematics, Statistics, Computer Science, Physics, Chemistry, Biomedical Engineering)
• Mathematical foundations to support the above, including calculus, probability theory and linear algebra
• Familiarity with deep learning fundamentals, including model architectures, optimisation, evaluation and scaling
• Experience in visual generative AI, visual understanding or world models
• Evidence of research ability, such as conference or workshop submissions, publications, technical reports, open-source projects or impactful industrial research
• Strong Python programming skills using a modern deep learning framework, e.g. PyTorch or JAX
• Experience with multimodal reasoning or generation, action-conditioned models, embodied AI, robotics or autonomous systems
• Lower-level programming for hardware efficiency, e.g. C++/CUDA/Triton
• Practical familiarity with hardware considerations for deep learning, such as parallelism, memory hierarchy, vector and matrix engines, data movement, bandwidth limits and performance bottlenecks
• Practical familiarity with deep learning software stacks, such as graph compilation, kernel fusion, XLA/ATen operations, streams and asynchronous execution