Research Scientist (Visual Generative AI & World Models)

Join Graphcore as a Research Scientist specializing in Visual Generative AI and World Models. In this role, 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 position offers a supportive and collaborative team environment, flexible working arrangements, private medical insurance, a pension plan, and opportunities for learning and development.

• Advancing AI research in visual generative modeling, multimodal learning, and world models.
• Exploring new model architectures, training methods, and deployment strategies for applications in embodied AI and robotics.
• Collaborating with researchers, software engineers, and silicon teams to shape future Graphcore hardware and software systems.
• We are looking for researchers and engineers with the theoretical depth, practical judgement and implementation skills to turn ambitious ideas into rigorous experiments, publications and technical insights that influence the future of AI compute
• Perhaps the best way to get an idea of what we’re all about is to read one of our papers or an article on our blog. If you’re excited to work at the cutting edge of AI and want to help shape the hardware and software systems that drive the future of AI compute, we’d love to hear from you!
• 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
• Strong Python programming skills using a modern deep learning framework, e.g. PyTorch or JAX
• Experience in visual generative AI, visual understanding or world models
• Ability to design, execute, analyse and clearly communicate ML experiments
• Evidence of research ability, such as conference or workshop submissions, publications, technical reports, open-source projects or impactful industrial research
• 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