Principal Reliability Scientist

Principal Reliability Scientist

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.

As part of the Soft Bank Group, Graphcore is a member of an elite family of companies responsible for some of the world's most transformative technologies. Together, they share a bold vision: to enable Artificial Super Intelligence and ensure its benefits are accessible to everyone.

Graphcore's 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.

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.

The Quality team within Manufacturing Operations is responsible for ensuring product robustness, reliability and lifecycle performance across Graphcore's 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.

· 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 targets 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

Qualification:

· 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