Graduate Software Engineer (AI/ ML Test Systems 2027)

• Put your software skills to work testing how AI and machine learning workloads run on Graphcore accelerator hardware
• As a Graduate Software Engineer in AI/ML Test Systems, you’ll create representative machine learning workloads and analyse their numerical accuracy and performance
• You’ll design tests and help improve testing strategies and infrastructure across simulated and physical hardware
• You’ll also build continuous integration and continuous delivery (CI/CD) pipelines, investigate difficult problems and turn your findings into measurable improvements
• Your work will connect machine learning frameworks and models with the hardware beneath them, helping improve software quality and customer experience
• The Software QA team combines software engineering, automation and data analysis to improve product quality and customer experience
• We work extensively with industry-standard machine learning frameworks and models, including contributions to upstream open-source projects
• Our team works in small squads, each with ownership of a particular area of software quality
• As a graduate, you’ll have the opportunity to explore the breadth of our work, gain practical experience across different areas and discover what interests you before settling into a squad
• You’ll work alongside engineers who investigate problems, share findings and collaborate closely with development teams to improve quality, security and maintainability
• Test results, performance data and the needs of the wider software organisation guide our decisions
• You’ll have a buddy to help you find your feet and a manager who will support your development, explore your career goals with you and help make time for learning
• You’ll be encouraged to ask questions, challenge existing approaches and take initiative, with experienced colleagues to turn to for guidance as you build confidence and take ownership of your work
• During the two-year programme, you’ll build practical experience across machine learning, testing, automation and performance analysis
• Creating workloads and analysing results will help you understand how software behaves on accelerator hardware
• Debugging issues will develop your problem-solving skills and help you connect numerical accuracy and performance to the systems running a model
• Work on test infrastructure and CI/CD will show you how automated checks support software development across simulated and physical platforms
• Sharing findings with development teams will help you explain technical problems clearly and turn evidence into improvements

Benefits

• Work-Life Balance: We believe you should have a good work-life balance. Everyone’s situation is different, so we offer flexible working arrangements and start and finish times, allowing you to choose when to work from home or come into the office
• Healthcare: We offer Private Medical Insurance (PMI) to our employees to help them take care of their health and that of their loved ones
• Pension: We believe that our employees deserve the best when it comes to planning for their future
• Income Protection: Graphcore understands that unforeseen circumstances can happen, and we want to ensure our employees have the necessary financial security in place
• Progression: We prioritise learning and development as an integral part of our culture at Graphcore. Our Talent Development team is dedicated to equipping all our employees with the necessary tools, resources, and support to enhance their skills and reach their full potential in their careers
• Wellbeing: We want to ensure that our employees have the resources and support they need to prioritise their health and wellbeing

• Strong Python skills and proficiency in C++
• Bachelor’s/Master’s degree in Computer Science, Maths, Machine Learning, Data Science or a related field
• Experience with distributed workload management systems or machine learning operations (MLOps) pipelines would also be useful. These are additional strengths, rather than essential requirements
• Experience working with Linux operating systems and the ability to learn new technologies quickly
• Exposure to machine learning frameworks such as PyTorch, JAX, Triton or TensorFlow, test automation, CI/CD, data analytics, hardware architectures or compilers would be useful

• Start date: September 2027
• Application deadline: Midnight, 29th November 2026