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The Silicon Physical Design Engineer will develop AI chips focusing on performance and power efficiency, collaborating within the team and across departments.

Lead post-silicon validation for AI SoCs, develop strategies for reliability, debug complex issues, and foster team collaboration and automation.

Design, implement, test and document a large-scale collectives communication simulator for new AI hardware. Collaborate with cross-functional teams, troubleshoot complex issues, ensure hardware integration, mentor junior engineers, and participate in agile development to deliver high-performance communication primitives.

Develop manufacturing test solutions and diagnostics for AI compute hardware across silicon, boards, servers, and racks. Build test software and automation, design test approaches, lead complex hardware debug, and collaborate with firmware, software, and manufacturing teams to improve testability and product quality.

Responsible for leading post-silicon validation for AI SoCs, ensuring reliability and quality of new silicon. Collaborates across teams to resolve issues and improve validation processes.

Drive automation-based validation execution for AI hardware platforms, defining validation strategies and ensuring system readiness across blade and rack levels.

Build and maintain tools and services for software build, test, deployment and release. Deploy and operate cloud-native services (Kubernetes/Docker), manage infrastructure as code, improve CI/CD pipelines, and support junior engineers while ensuring reliability and reducing toil.

Develop, operate, and maintain Kubernetes-based infrastructure and tooling to support build, test, deployment, and scaling of Graphcore's ML software stack. Manage cloud infrastructure (infrastructure-as-code), CI/CD practices, and platform services used by software teams, focusing on reliability, automation, and reducing operational toil.

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.

Develop and maintain internal build, test, and integration tooling for the ML software stack. Improve release processes, guide developers on dependency and build best practices, collaborate with QA and dev teams to resolve integration issues, and contribute fixes to the ML software stack in Linux environments.