Top AI & Machine Learning Jobs

Join Graphcore's Frameworks team to design, implement, optimize and support PyTorch (and other ML framework) integrations for Graphcore accelerators. Deliver features, write tests and documentation, review code, resolve performance issues, collaborate with upstream open-source projects, and mentor teammates.

Analyze data center, system, and silicon telemetry to detect anomalies, forecast failures, and optimize AI infrastructure. Build predictive models, feature pipelines, and analytics foundations using time-series and large-scale telemetry data. Partner with platform, hardware, and software engineers to improve observability, data quality, system reliability, and automated engineering decisions.

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.

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.

Define and drive the software architecture for Graphcore's ML accelerator stack. Collaborate with HW and software teams to document, communicate, prototype, and maintain end-to-end architecture, ensuring performance, reliability, maintainability, security, and alignment with product goals.

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

Build, own, and maintain infrastructure, tools, and services to support ML research and engineering. Deploy and maintain services with Kubernetes and Docker, manage cloud infrastructure (Terraform), and improve CI/CD, build, test, deployment, and productisation of ML software on HPC AI platforms.

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.

Define and integrate SoC subsystems for Graphcore AI acceleration platforms. Turn product requirements into architecture choices across processor, SoC and system levels. Specify interfaces, interconnects and subsystems, produce technical specifications, model and validate designs, and coordinate across design, verification, hardware and software teams to deliver multiple AI silicon projects.

Design and run ML experiments, implement and optimize algorithms for hardware efficiency, collaborate with researchers and engineers, and publish and present results at AI conferences.