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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Machine Learning Engineer (Large Systems) - **Company:** Graphcore - **Location:** Bristol, UK - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, C++ (Programming Language), Code Review, Nvidia CUDA, Distributed Computing Environment, InfiniBand, Python (Programming Language), Machine Learning, Open Source Technology, Performance Tuning, Software Engineering, Cloud Platform System, Pytorch, Large Language Models, Deep Learning, Kubernetes, Information Technology, Hardware Acceleration, Machine Learning Operations, Software Library - **Published:** July 8, 2026 - **Apply:** https://uk.indeed.com/viewjob?jk=46020cc07569f83c ## About the Role * Bachelor/Master's/PhD or equivalent experience in Machine Learning, Computer Science, Maths, Data Science, or related field. * Proficiency in deep learning frameworks like PyTorch/JAX. * Strong Python or C++ software development skills * Expertise in deep learning from model training to optimisation and evaluation. * Experience in distributed training or inference of ML models across 64+ accelerators. * Capable of designing, executing and reporting from ML experiments. * Developed deep understanding of performance bottlenecks and how to overcome them. * Ability to move quickly in a dynamic environment * Enjoy cross-functional work collaborating with other teams. * Strong communicator - able to explain complex technical concepts to different audiences. Desirable: * Experience in one or more of: + MLOps for Kubernetes-based clusters + Building production systems with large language models + Efficient computing based on low-precision arithmetic. * Experience writing C++/Triton/CUDA kernels for performance optimisation of ML models. * Familiarity with HPC systems and networking including Infiniband, NVLink, RoCE technologies. * Have contributed to open-source projects or published research papers in relevant fields. * Knowledge of cloud computing platforms. * Keen to present, publish and deliver talks in the AI community. ## Description 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 Team 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! Responsibilities and Duties * Implement latest machine learning models and optimise them for performance and accuracy, scaling to 1000s of accelerators. * Test and evaluate new internal software releases, provide feedback to software engineering teams, make necessary code fixes, and conduct code reviews. * Benchmark models and key ML techniques to identify performance bottlenecks and improve model efficiency. * Design and conduct experiments on novel AI methods, implement them and evaluate results. * Collaborate with Research, Software, and Product teams to define, build, and test Graphcore's next generation of AI hardware. * Engage with AI community and keep in touch with the latest developments in AI. ## Related Videos - [Understanding Kubernetes in a visual way](https://www.wearedevelopers.com/videos/100085-understanding-kubernetes-in-a-visual-way) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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Now What?](https://www.wearedevelopers.com/videos/1590-your-next-ai-needs-10-000-gpus-now-what) - [Geometric deep learning for drug discovery](https://www.wearedevelopers.com/videos/264-geometric-deep-learning-for-drug-discovery) - [Serverless deployment of (large) NLP models ](https://www.wearedevelopers.com/videos/158-serverless-deployment-of-large-nlp-models) - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Got AI ideas but no money? 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