Job Summary
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 the advancement of 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.
The Team
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 be at the forefront of AI supported by new hardware and develop your skills in that area, we'd love to hear from you!
Responsibilities and Duties
- 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.
Candidate Profile
Essential:
- Master's, PhD or equivalent experience in a technical field (e.g., Maths, Statistics, Computer Science, Physics, Chemistry, Biomedical Engineering).
- 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.
Desirable
- Experience in one or more of: {distributed computing, efficient computing based on low-precision arithmetic, deep learning models including large generative models for language, vision and other modalities, machine learning for molecules and proteins (ideally with some background in chemistry and biological sciences)}.
- 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 software stacks for deep learning – compilation, kernel fusion, XLA/ATen ops, streams, and asynchronous execution.
- Experience submitting papers to international scientific conferences or workshops.
Benefits
In addition to a competitive salary, Graphcore offers flexible working, a generous annual leave policy, private medical insurance and health cash plan, a dental plan, pension (matched up to 5%), life assurance and income protection. We have a generous parental leave policy and an employee assistance programme (which includes health, mental wellbeing, and bereavement support). We welcome people of different backgrounds and experiences; we’re committed to building an inclusive work environment that makes Graphcore a great home for everyone. We offer an equal opportunity process and understand that there are visible and invisible differences in all of us. We can provide a flexible approach to interview and encourage you to chat to us if you require any reasonable adjustments.
