Graphcore

Artificial Intelligence

PrincipalReliabilityScientist

United States
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Senior candidates.

The Brief

“Principal Reliability Scientist at Graphcore. Skills: reliability engineering, reliability science, AI hardware, complex systems, reliability modelling, experimental design, statistical data analysis, root cause investigation. leading reliability activities across complex, high-performance systems. uses experimental data and advanced modelling to inform design decisions”

What You'll Achieve.

inform design decisions; validate product reliability; optimise serviceability strategies; drive engineering decisions; influence architecture and component selection; driving corrective and preventative actions; Contribute to the evolution of reliability tools, processes and best practices

Industry & Context.

Artificial Intelligence
Problems you'll solve

problem-solving skills with the ability to lead technically challenging investigations independently

What They're Looking For.

Must Have

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, 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

Nice to Have

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

What You'll Do.

leading reliability activities across complex

high-performance systems

uses experimental data and advanced modelling to inform design decisions

validate product reliability

optimise serviceability strategies

including spares provisioning

Define and refine reliability requirements across silicon

board and system levels

Apply advanced reliability methodologies to highly innovative systems

Design and execute experiments to generate high-quality reliability and performance data

field and manufacturing data to quantify reliability metrics

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

How You'll Work.

Team & Collaboration

Working closely with established reliability experts and cross-functional teams; works closely with technology research, chip, board, system design, platform and operations teams; working in partnership with research and design teams; Collaborate with chip, board and system design teams; driving corrective and preventative actions across teams; Ability to operate effectively in complex, cross-functional environments with multiple stakeholders; ability to influence design and operations teams using data-driven insights

Communication Scope

Excellent communication skills, with the ability to influence design and operations teams using data-driven insights; Communicate complex reliability concepts, risks and recommendations clearly to a wide range of stakeholders

Full Job Description

About us 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 SoftBank 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. 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. The Team 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. 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,

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