Research Scientist; Embodied AI & Models

Research Scientist (Embodied AI & World Models)

Bristol, UK

Graphcoreis one of the world’s leading innovators in Artificial Intelligence compute.

It is developing hardware, software and systems infrastructure that will unlock the next generation of AI breakthroughs and power the widespread adoption of AI solutions across every industry.

As part of the Soft Bank Group,Graphcoreis a member of an elite family of companies responsible for some of the world’s most transformative technologies. Together, they share a bold vision: to enable Artificial Super Intelligence and ensure its benefits are accessible to everyone.

Graphcore’s teams are drawn from diverse backgrounds and bring a broad range of skills and perspectives. A melting pot of AI research specialists, silicon designers, software engineers and systems architects, Graphcore enjoys a culture of continuous learning and constant innovation.

As a researcher at Graphcore, you will contribute to the advancement of AI research, investigating new ideas that push the limits on important AI/ML problems. Specialised hardware has been the key driver of the progress of AI 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 exciting field.

As such, we’re looking for candidates who are keen scientists and engineers, with the theoretical and practical skills needed for impactful AI research.

We are looking for researchers with experience of AI in low-power, edge and embodied scenarios such as robotics, autonomous driving and augmented/virtual reality. We are interested in the training and deployment of multimodal AI models in these contexts, covering areas such as world models, real-time computer vision, generating and reasoning over audio/video streams.

Graphcore Research participates in both fundamental and applied research, to characterise the computational requirements of machine intelligence and to demonstrate how hardware can drive the next generation of innovative AI models. We publish at leading AI/ML conferences (NeurIPS, ICML, ICLR) as well as specialist workshops, and collaborate with other research teams and organisations across the world.

We pride ourselves on being a supportive and collaborative team, where we organise around our individual research interests to solve problems together in domains such as efficient compute, model scaling and distributed training and inference of AI models for multiple modalities and applications, including for sequence- and graph-based data. We’re based across London, Cambridge and Bristol, with projects and discussions that involve all our locations.

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 supported by new hardware and want to develop your skills in this area, we’d love to hear from you!

• Generate AI/ML ideas, design experiments, implement them & evaluate results.
• Prepare, submit & present your work to AI conferences and workshops.
• Collaborate with researchers, silicon and software engineers at Graphcore to help define, build and test Graphcore’s next generation of AI hardware.

• Master’s, PhD or equivalent experience in a technical discipline (e.g., Maths, Statistics, Computer Science, Physics, Chemistry, Biomedical Engineering).
• Experience in embodied AI, world models or robotics.
• Python programming in a modern deep learning framework, e.g. PyTorch or JAX.
• Familiar with deep learning fundamentals: models, optimisation, evaluation and scaling.
• Capable of designing, executing and reporting from ML experiments.
• Mathematics skills to support the above: calculus, probability theory and linear algebra.
• Experience submitting papers to international scientific conferences or workshops.
• Lower-level programming for hardware efficiency, e.g. C++/CUDA/Triton.
• Practical familiarity with hardware capabilities for deep learning – threads, caches, vector & matrix engines, data dependencies, bus widths and throttling.
• Practical familiarity with…