Staff Software Engineer (ML QA)

About Graphcore At Graphcore, we’re building the future of AI compute. We’re a team of semiconductor, software and AI experts, with deep experience in creating the complete AI compute stack - from silicon and software to infrastructure at datacenter scale. As part of the SoftBank Group, backed by significant long-term investment, we are delivering key technology into the fast-growing SoftBank AI ecosystem. To meet the vast and exciting AI opportunity, Graphcore is expanding its teams around the world. We are bringing together the brightest minds to solve the toughest problems, in a place where everyone has the opportunity to make an impact on the company, our products and the future of artificial intelligence Job Summary Applicants for this role should have strong experience designing, developing, and maintaining high-quality software systems. The role focuses on testing and validating a complex machine learning software stack, with particular emphasis on software architecture, automation, and engineering best practices. The ideal candidate is an experienced software engineer who values code quality, testability, and long-term maintainability, and enjoys building systems that other engineers rely on. This person will be comfortable working across large codebases, contributing to CI/CD infrastructure, and shaping technical direction through thoughtful design and mentoring in a technically demanding environment spanning ML frameworks, infrastructure, and AI accelerator hardware The Team The ML QA team is composed of highly skilled software engineers with a strong focus on automation, software quality, and data-driven validation. The team works closely with industry-standard machine learning frameworks and models , contributing to upstream open-source projects and collaborating across the wider software organization. Operating in a fast-paced environment, the team plays a critical role in ensuring reliability, performance, and maintainability across the ML software stack, helping to deliver robust and high-quality products to customers. Responsibilities and Duties Design, implement, and maintain robust test infrastructure and automation for a complex ML software stack. Architect and evolve test frameworks and tooling with a focus on scalability, maintainability, and developer experience. Build and maintain CI/CD pipelines targeting simulators, emulators (e.g. QEMU), and physical hardware. Create representative ML workloads and gain insights from their execution. (Numerical accuracy, performance analysis and benchmarking). Work closely with all Software development teams, supporting a culture of quality, security and maintainability. Review code and designs, setting a high bar for software engineering best practices. Mentor and support junior engineers, helping raise the overall technical capability of the team. Evaluate existing test strategies and infrastructure, identifying gaps and driving improvements aligned with team and organizational goals. Candidate Profile Essential: Experience in production-quality software engineering roles. Strong software design and architecture skills, with experience working on large or complex systems. Strong proficiency in Python, including experience building and maintaining production codebases. Solid experience with CI/CD systems and automated testing (preferably GitHub-based workflows). Experience working in Linux environments. Familiarity with C or C++, with the ability to read, debug, and reason about low-level code when needed. Proven ability to mentor junior engineers and influence engineering practices within a team. Strong problem-solving skills and a proactive, self-directed approach to work. Bachelor/Master's/PhD or equivalent experience in Computer Science, Maths, Machine Learning, Data Science, or related field. Desirable Exposure to machine learning frameworks such as PyTorch, JAX, Triton, TensorFlow Experience with distributed workload management systems such as Kubernetes, VLLM,