Research Scientist (Visual Generative AI & World Models)
Share
Job Location:
Monthly Salary:
Posted on:
30+ days ago
Vacancies:
1 Vacancy
Job Summary
About Graphcore
At Graphcore were building the future of AI compute. Were 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 deploymentstrategies with applications in embodied AI robotics and autonomous systems. Example research directions could include efficientvideo generation diffusion and flow-based models multimodal representation learning world models for agents or analysis of howemerging 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 AIprogress over the last decade and we believe that hardware-aware AI algorithms and AI-aware hardware developments will continueto be critical to advancing this field. We are looking for researchers and engineers with the theoretical depth practical judgement andimplementation skills to turn ambitious ideas into rigorous experiments publications and technical insights that influence the future ofAI compute.
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 leadingAI/ML conferences including NeurIPS ICML and ICLR as well as specialist workshops and collaborate with other research teams andorganisations across the world.
We pride ourselves on being a supportive and collaborative team where we organise around individual research interests and solveproblems together. Our work spans efficient compute model scaling distributed training and inference and AI models for multiplemodalities and applications including sequence- and graph-based data. Were based across London Cambridge and Bristol withprojects and discussions that involve all our locations.
Perhaps the best way to get an idea of what were all about is to read one of our papers or an article on our blog. If youre excitedto work at the cutting edge of AI and want to help shape the hardware and software systems that drive the future of AI compute wedlove to hear from you!
Responsibilities and Duties
- 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.
About you:
Essential:
- Masters 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.
- Strong Python programming skills using a modern deep learning framework e.g. PyTorch or JAX.
- 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.
Desirable:
- 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.
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 offer a range of healthy food and snacks at our central Bristol office and have our own barista bar! We welcome people of different backgrounds and experiences; were 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.
Applicants for this position must hold the right to work in the UK. Unfortunately at this time we are unable to provide visa sponsorship or support for visa applications
Required Experience:
IC
Key Skills
About Company
TourManager allows you to seamlessly manage your touring schedule across multiple tours, locations and groups
