Top Engineering Jobs

Develop and maintain internal tooling and processes for building, testing, integrating, and releasing the ML software stack. Collaborate with developers, QA, and architects to ensure reproducible, secure builds, resolve integration issues, and contribute fixes to the ML stack.

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.

Build and maintain tools and services to support software teams; deploy and manage Kubernetes infrastructure for development, testing, and scaling; manage cloud infrastructure using IaC; improve CI/CD, build, test, deployment and observability for ML/HPC software stacks.

Develop and maintain tools and services for build, test, deployment, and release of ML software. Deploy and operate services with Kubernetes and Docker, manage cloud infrastructure using Terraform, support CI/CD production deployments, mentor junior engineers, and ensure high reliability with minimal toil for distributed/HPC systems.

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.

Lead and build an SLT test engineering team to develop system-level test capabilities for multi-die AI processors. Define manufacturing test solutions, design test fixtures, write production and characterization test programs on SLT platforms, collaborate across silicon, test, DFT and manufacturing teams, transfer solutions to volume manufacturers/OSATs, and support bring-up, failure analysis and yield improvement.

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 and automated pipelines that turn upstream software into production-ready Graphcore platform releases. Develop and run validation and test suites, diagnose build/test failures, introduce controlled configuration changes, and improve release automation and engineering quality.

Build and maintain Graphcore's Linux distribution pipeline to produce production-ready system images. Improve automated CI/CD pipelines, run Linux test suites, diagnose build and validation failures, and introduce controlled configuration changes with evidence-led validation. Define reliable processes for release preparation and deployment.

Design, build and validate Graphcore Linux distribution images and automated pipelines. Run Linux test suites, diagnose build and test failures, introduce controlled configuration changes, and improve release processes to produce dependable platform releases for AI systems.