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Investigate a next-generation AI computing architecture across rack and data-centre scales. Design experiments, benchmark AI workloads, analyse hardware-software interactions, and evaluate system performance, scalability, and reliability. Develop automation and tooling to collect, analyse, and visualise system metrics, communicating findings with clear, reproducible evidence while collaborating across engineering teams.

Develop internal tooling and technical processes for building, testing, integrating, and releasing Graphcore’s machine learning software stack. Guide developers on build practices and dependency management, collaborate with QA and software teams to resolve integration issues, contribute software fixes, and help ensure reproducible builds in secure, sandboxed environments.

Build and operate Kubernetes-based infrastructure and tooling to support development, testing, deployment and scaling of Graphcores ML software. Manage cloud infrastructure, CI/CD practices, and collaborate with software teams to improve reliability and observability of HPC AI platforms.

Design, deploy, and maintain tools and services for build, CI/CD, packaging, and releases. Manage cloud and Kubernetes infrastructure, use IaC, reduce toil, and support junior engineers for reliable ML software delivery.

Develop and maintain internal build, test, and integration tooling for the ML software stack. Build release processes, assist dependency management, guide developers on build best practices, collaborate with QA and engineers to resolve integration issues, and contribute fixes to the ML software stack.

Build and maintain the Linux distribution foundation for Graphcore platform releases: develop automated pipelines, run and diagnose Linux test suites, create system images, and validate upstream software changes to produce production-ready releases.

Build and maintain infrastructure, tools and services to support AI research and engineering. Deploy and operate services with Kubernetes and Docker, manage cloud infrastructure (Terraform/AWS), support CI/CD, and enable ML application deployment, orchestration, and accelerator hardware management.

Conduct hardware-aware AI research: generate ideas, design and run ML experiments, implement performant scalable systems, publish and present results, and collaborate with researchers, silicon and software engineers to guide next-generation AI hardware.

The role involves developing cloud platforms, deploying AI services, maintaining systems, and integrating third-party products in cloud environments.

Develop, implement and optimise large-scale ML models for Graphcore hardware; benchmark and scale training/inference across thousands of accelerators; test internal releases, fix and review code; design and run experiments; collaborate with Research, Software and Product teams; engage with the AI community.