ML QA Technical Product Owner

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. The Team The ML QA team is responsible for validating the machine learning software stack running on Graphcore hardware. The team works across integration testing, feature validation, performance benchmarking, and end-to-end workload testing, covering multiple layers of the software stack including ML frameworks, runtime behaviour, distributed execution, and system-level functionality. The team collaborates closely with software engineering teams throughout the development lifecycle, helping define validation strategies early and implementing the test coverage required to validate correctness, functionality, scalability, and performance. The ML QA team also owns performance-focused validation, including benchmark execution, regression analysis, and reporting across real-world ML workloads and extracted model subgraphs. As Technical Product Owner for ML QA, you will help coordinate and prioritise this work, ensuring the team has clear direction, well-defined deliverables, and alignment with wider software roadmap objectives . Responsibilities and Duties Own and Shape the Component Backlog Create and maintain team roadmaps for the Program Increment (PI) and long term plan to help visualise the current backlog and communicate status and progress to all stakeholders. Translate feature-level intent into actionable Software QA work and feedback team intent and challenges to help shape the features with the Technical Product Manager. Maintain a clear, prioritised backlog of Epics and Stories for the Software QA team. Ensure all work is clearly linked to higher-level product outcomes. Work with engineers and technical leads to refine acceptance criteria, validation scope, and delivery expectations. Support Delivery Across Sprints and Planning Increments Actively support sprint planning, reviews, demos, providing visual outputs that demonstrate progress against the plan, learnings and changes. Involve the team in the right discussions to ensure desired outcomes are realistic, and the team’s ability to deliver them are clearly communicated. Ensure the backlog is sufficiently maintained to prepare for PI planning, identifying dependencies and risks early and aligning with other teams on the scope taken into planning. Ensure your team’s work aligns with agreed PI objectives. Coordinate Validation Across Teams Work closely with Technical Product Owners, Product Managers, engineering teams, and technical leads to manage dependencies and alignment. Coordinate feature validation planning with teams working across ML frameworks, runtime systems, performance tooling, distributed execution, and infrastructure. Help ensure testing, benchmarking, and integration activities are planned early as part of feature development. Facilitate communication between teams to identify risks, blockers, and changing priorities. Enable Effective Technical Collaboration Develop sufficient technical understanding of the ML software stack and validation workflows to engage effectively with engineers and stakeholders. Help prioritise validation coverage for new features, performance improvements, and software changes. Support constructive technical discussions while balancing delivery priorities and quality expectations. Help remove organisational blockers and