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Graphcore, based in London, is seeking a Senior Machine Learning Engineer for its Applied AI team to advance AI models on our specialised hardware. You will develop and optimise models, work with software and research teams, and push performance and scalability across large-scale systems.
You will contribute to scaling experiments, benchmarking, and deploying techniques that improve efficiency, accuracy, and throughput on Graphcore hardware, while staying aligned with the latest AI advances.
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.
As a Senior Machine Learning Engineer in the Applied AI team at Graphcore, you will contribute to advancing AI technology by developing and optimising AI models tailored to our specialised hardware. You will work on large scale systems where performance is critical to the success of our projects. Working closely with the Software development and Research teams, you will play a critical role in identifying opportunities to innovate and differentiate Graphcore’s technology. We seek engineers with strong technical skills and an understanding of AI model implementation at scale, eager to make a tangible impact in this rapidly evolving field.
The Applied AI team’s role is to be proxies for our customers; we need to understand the latest AI models, applications, and software to ensure that Graphcore’s technology works seamlessly with the AI ecosystem and at scale. We build reference applications, contribute to key software libraries (e.g., optimising kernels for efficiency on our hardware), and collaborate with the Research team to develop and publish novel ideas in domains such as efficient compute, model scaling and distributed training and inference of AI models for multiple modalities and applications.
If you’re excited about advancing the next generation of AI models on cutting‑edge hardware, we’d love to hear from you!