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Validate and benchmark ML models on Graphcore's software/hardware stack. Create automated benchmarking pipelines, run open-source models, design targeted tests for numerical precision, quantisation, attention, distributed execution, and debug performance and correctness issues across frameworks and environments.

Design and build test infrastructure, automation, and tooling to validate ML software stacks and hardware; shape test architecture across CI pipelines, frameworks, simulators, emulators, and large ML workloads; mentor engineers and own complex validation projects.

Validate and benchmark ML models and stacks on Graphcore hardware. Build automated benchmarking pipelines, run open-source models, create targeted tests for numerical precision, quantisation, distributed execution and subgraphs, and debug performance and correctness across frameworks and execution 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.

The Senior Systems Engineer focuses on analyzing performance data from systems, ensuring stability for production, and using engineering judgment for decision-making in a high-performance computing context.

Develop, manage, and sustain tools and services for software build, test, deployment, and release. Build and maintain API integrations and automation workflows, support Linux-based environments, and improve reliability while reducing operational toil. Mentor junior and graduate engineers and collaborate across engineering and security teams.

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.