Graphcore
AI compute
ResearchScientist(VisualGenerativeAI&WorldModels)
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Research Scientist (Visual Generative AI & World Models) at Graphcore. Skills: visual generative AI, multimodal modelling, world models, deep learning, Python, PyTorch, JAX. 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”
What You'll Achieve.
publications; technical insights that influence the future of AI compute
Industry & Context.
solve the toughest problems; solve problems together
hold the right to work in the UK, unable to provide visa sponsorship or support for visa applications
What They're Looking For.
Must Have
Master’s, PhD or equivalent experience in a technical discipline (e. g. , Mathematics, Statistics, Computer Science, Physics, Chemistry, Biomedical Engineering), Experience in visual generative AI, visual understanding or world models, Python programming skills using a modern deep learning framework, Familiarity with deep learning fundamentals, including model architectures, optimisation, evaluation and scaling, Ability to design, execute, analyse and clearly communicate ML experiments, Mathematical foundations to support the above, including 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
Nice to Have
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
What You'll Do.
Develop and evaluate new ideas in visual generative AI
multimodal modelling and world models
from initial hypothesis through experiment design
analysis and publication
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
future Graphcore hardware and software systems
How You'll Work.
Team & Collaboration
Work with researchers, software engineers and silicon teams; collaborate with other research teams and organisations across the world; supportive and collaborative team; organise around individual research interests and solve problems together
Communication Scope
clearly communicate ML experiments; present your work to AI conferences and workshops
Full Job Description
About Graphcore At Graphcore, we’re building the future of AI compute. We’re a team of semiconductor, software and AI experts, with deep experience in creating the complete AI compute stack - from silicon and software to infrastructure at datacenter scale. As part of the SoftBank Group, backed by significant long-term investment, we are delivering key technology into the fast-growing SoftBank AI ecosystem. To meet the vast and exciting AI opportunity, Graphcore is expanding its teams around the world. We are bringing together the brightest minds to solve the toughest problems, in a place where everyone has the opportunity to make an impact on the company, our products and the future of artificial intelligence. Job Summary As a Research Scientist at Graphcore, you will advance AI research at the intersection of visual generative modelling, 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. Example research directions could include efficient video generation, diffusion and flow-based models, multimodal representation learning, world models for agents, or analysis of how emerging generative AI workloads influence future AI accelerators. This role sits at the interface between frontier model research and AI hardware. Specialised hardware has been a key driver of AI progress over the last decade, and we believe that hardware-aware AI algorithms and AI-aware hardware developments will continue to be critical to advancing this field. 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. The Team Graphcore Research participates in both fundamental and applied research to characterise the computational requirements o
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