Principal Reliability Scientist

About the Company

Graphcore is one of the world’s leading innovators in Artificial Intelligence compute. 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.

Responsibilities

• Define and refine reliability requirements across silicon, board and system levels
• Apply advanced reliability methodologies to innovative systems, including liquid-cooled architectures
• Design and execute experiments to generate high-quality reliability and performance data
• Analyse experimental, field and manufacturing data to quantify metrics such as MTBF, MTTR, RAS, and SER
• Use data-driven insights to inform product design trade-offs and spares provisioning strategies
• Collaborate with chip, board and system design teams to influence architecture and component selection
• Support development of system-level reliability models incorporating thermal, mechanical and fluid behaviour
• Lead complex root cause investigations into reliability issues
• Communicate complex reliability concepts, risks and recommendations to stakeholders

Requirements

• 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 or AI hardware
• Experience with reliability modelling, experimental design and statistical data analysis
• Proven ability to interpret experimental reliability data to drive engineering decisions
• Experience with key reliability metrics such as MTBF, MTTR, RAS and failure rate analysis
• Ability to operate effectively in complex, cross-functional environments
• Strong problem-solving skills and ability to lead technical investigations independently
• Excellent communication skills to influence design and operations teams

Preferred Qualifications

• Experience with liquid cooling systems, fluid dynamics or thermally complex hardware environments
• Knowledge of soft error mechanisms and SER modeling
• Experience contributing to reliability strategy, processes or tooling improvements