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About us聽
Graphcore is one of the world鈥檚 leading innovators in Artificial Intelligence compute.聽
It is developing hardware, software and systems infrastructure that will unlock the next generation of AI breakthroughs and power the widespread adoption of AI solutions across every industry.聽
As part of the SoftBank Group, Graphcore is a member of an elite family of companies responsible for some of the world鈥檚 most transformative technologies. Together, they share a bold vision: to enable Artificial Super Intelligence and ensure its benefits are accessible to everyone.聽
Graphcore鈥檚 teams are drawn from diverse backgrounds and bring a broad range of skills and perspectives. A melting pot of AI research specialists, silicon designers, software engineers and systems architects, Graphcore enjoys a culture of continuous learning and constant innovation.聽
Job Summary聽
Reporting to the Quality leadership within Manufacturing Operations, the Senior Reliability Scientist is responsible for leading reliability activities across complex, high-performance systems. Working closely with established reliability experts and cross-functional teams, this role uses experimental data and advanced modelling to inform design decisions, validate product reliability and optimise serviceability strategies, including spares provisioning.聽
The Team聽
The Quality team within Manufacturing Operations is responsible for ensuring product robustness, reliability and lifecycle performance across Graphcore鈥檚 hardware portfolio. The team includes experienced reliability specialists and works closely with technology research, chip, board, system design, platform and operations teams to translate reliability insights into actionable improvements across the product lifecycle. 聽
Responsibilities and Duties:
路 Define and refine reliability requirements across silicon, board and system levels, working in partnership with research and design teams聽
路 Apply advanced reliability methodologies to highly innovative systems, including challenges associated with liquid-cooled architectures and fluid dynamics聽
路 Design and execute experiments to generate high-quality reliability and performance data, ensuring statistical rigour and relevance聽
路 Analyse experimental, field and manufacturing data to quantify reliability metrics such as MTBF, MTTR, RAS characteristics and soft error rates (SER)聽
路 Use data-driven insights to inform product design trade-offs, reliability targets and spares provisioning strategies聽
路 Collaborate with chip, board and system design teams to influence architecture and component selection based on reliability considerations聽
路 Support development of system-level reliability models incorporating thermal, mechanical and fluid behaviour聽
路 Lead complex root cause investigations into reliability issues, driving corrective and preventative actions across teams聽
路 Contribute to the evolution of reliability tools, processes and best practices within the organisation聽
路 Communicate complex reliability concepts, risks and recommendations clearly to a wide range of stakeholders聽
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Qualifications:聽
- Strong background in reliability engineering or reliability science within semiconductor, hardware or complex systems environments
- Experience of physics-of-failure approaches in high-performance computing, AI hardware or related domains
- Experience with reliability modelling, experimental design and statistical data analysis
- Proven ability to work with and interpret experimental reliability data to drive engineering decisions聽
- Experience with key reliability metrics such as MTBF, MTTR, RAS and failure rate analysis
- Ability to operate effectively in complex, cross-functional environments with multiple stakeholders
- Strong problem-solving skills with the ability to lead technically challenging investigations independently
- Excellent communication skills, with the ability to influence design and operations teams using data-driven insights聽
Preferred Qualification:聽聽
路 Experience with liquid cooling systems, fluid dynamics or thermally complex hardware environments聽
路 Knowledge of soft error mechanisms and SER modeling
路 Experience contributing to reliability strategy, processes or tooling improvements聽
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