Principal Reliability Scientist

Job Summary

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.

Responsibilities and Duties

• Define and refine reliability requirements across silicon, board and system levels, working in partnership with research and design teams
• Apply advanced reliability methodologies to highly innovative systems, including challenges associated with liquid‑cooled architectures and fluid dynamics
• Design and execute experiments to generate high‑quality reliability and performance data, ensuring statistical rigour and relevance
• Analyse experimental, field and manufacturing data to quantify reliability metrics such as MTBF, MTTR, RAS characteristics and soft error rates (SER)
• Use data‑driven insights to inform product design trade‑offs, reliability target and spares provisioning strategies
• Collaborate with chip, board and system design teams to influence architecture and component selection based on reliability considerations
• Support development of system‑level reliability models incorporating thermal, mechanical and fluid behaviour
• Lead complex root‑cause investigations into reliability issues, driving corrective and preventative actions across teams
• Contribute to the evolution of reliability tools, processes and best practices within the organisation
• Communicate complex reliability concepts, risks and recommendations clearly to a wide range of stakeholders

Qualifications

• 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
• 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

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

Equal Employment Opportunity

As set forth in Graphcore’s Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law.