Lead reliability activities for high-performance AI systems, driving design decisions and optimising serviceability strategies.
Posted by employer 3 months ago
First seen on Joblaze 1 week ago
Last verified on the company career page 1 day ago
Skills & Technologies
Not disclosed in this posting: compensation, years of experience, work arrangement, visa sponsorship.
Joblaze summary
The Principal Reliability Scientist at Graphcore leads reliability initiatives for advanced AI hardware systems, focusing on experimental data and modeling to enhance product robustness. Key skills include reliability engineering, statistical analysis, and familiarity with metrics like MTBF and MTTR, particularly in high-performance computing environments. This role is ideal for experienced professionals with a strong background in reliability science who can navigate complex, cross-functional teams. Graphcore's culture emphasizes continuous learning and innovation, making it a dynamic environment for technical growth.
Joblaze insights
- Listed 9 days ago on Joblaze — tracked directly from Graphcore's career page.
- Graphcore has 196 other open roles including Senior Workplace Coordinator - Bengaluru, Technical Programme Manager , Manufacturing Operational Excellence Manager.
- 5144 active AI/ML roles on Joblaze right now.
- 28 active Reliability Engineering roles on Joblaze right now.
- 27 active fluid dynamics roles on Joblaze right now.
- Typical AI/ML salary we track runs $140,000–$231,000 (median $180,000, across 2237 roles). See AI/ML salary trends
Quick facts
- What's the tech stack?
- Joblaze extracted these technologies from the posting: AI/ML, Experimental Design, Reliability Engineering, fluid dynamics, liquid cooling systems, statistical data analysis.
- What seniority level is this role?
- Graphcore targets principal-level candidates for this position.
- Is this full-time or contract?
- Full-time for this Principal Reliability Scientist role at Graphcore.
From the original posting
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, 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