Requirements
- Experience working as a Product Owner (or similar role) in an agile environment
- Ability to communicate clearly with both technical and non-technical stakeholders
- Strong backlog management and refinement skills
- Proven experience in developing product vision and roadmaps
- Ability to understand and discuss technical concepts related to software development, testing, or infrastructure
- Strategic mindset with empathy and ability to bring calm, clarity and support
- Excellent facilitation skills, ability to guide discussions / events to conclusion
- Ability to simplify and present complex information
- Confidence working with technical teams and complex systems
- Experience coordinating work across multiple teams or components
- (Desirable) Certified Scrum Product Owner (CSPO) or Professional Scrum Product Owner (PSPO)
- (Desirable) Experience of working in SAFe / Scrum@scale
- (Desirable) Experience with Atlassian Tool Suite
- (Desirable) Experience working with machine learning software, ML infrastructure, or performance benchmarking environments
What the job involves
- The ML QA teamis responsible forvalidatingthe machine learning software stack running onGraphcorehardware
- 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 tovalidatecorrectness, 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 roadmapobjectives
- 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 toidentifyrisks, 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 improve coordination across teams and stakeholders
