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Design and maintain production-quality test infrastructure for Graphcore’s machine learning software stack. Build scalable test frameworks and CI/CD pipelines across simulators, emulators, and physical AI hardware. Create representative ML workloads, analyze execution results, improve testing and tooling, contribute to open-source projects, and mentor junior engineers. The role combines software architecture, automated testing, low-level debugging, and hardware-aware validation.

Design and maintain production-quality ML test infrastructure, CI/CD pipelines, automated testing systems, and representative workloads across simulators, emulators, and AI accelerator hardware. Analyze numerical accuracy and performance, improve testing and developer tooling, contribute to software architecture, and mentor junior engineers. The role requires strong Python, software design, Linux, CI/CD, automated testing, and C or C++ skills.

Conduct research on AI compute, machine learning algorithms, large language models, efficient inference, numerics, world models, molecule generation, or model safety. Implement models and algorithms in Python or C++, design and analyze experiments using frameworks such as PyTorch, and communicate findings through technical presentations or research papers under experienced mentorship.

Develop and optimize state-of-the-art machine learning models for specialized AI hardware and systems containing thousands of accelerators. Responsibilities include model training, benchmarking, profiling, kernel optimization, experimentation, software testing, code reviews, and collaboration with research, software, and product teams. The role focuses on performance, scalability, distributed training and inference, and advancing next-generation AI hardware and software.

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.

Plan and execute verification for complex silicon architecture: define coverage, generate tests, diagnose RTL/software failures, and improve shared verification infrastructure to ensure silicon quality before implementation.

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.