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Graphcore is building the future of AI compute infrastructure as part of the SoftBank Group. This role sits at the intersection of systems engineering and data analysis, focused on turning large-scale system measurements into actionable engineering decisions.
You will work with real performance data collected from clusters of machines running AI workloads. The core challenge is not data collection—it's interpretation: determining whether systems behave as expected, whether performance is stable enough for production, and whether systems meet production readiness criteria.
Responsibilities include:
- Analyzing detailed performance data from distributed, high-performance systems
- Understanding variability, repeatability, and distinguishing signal from noise
- Shaping measurement strategies and influencing how benchmarks are run
- Improving how results are interpreted and communicated to engineering teams
- Producing outputs that directly support real engineering decisions (not dashboards or reports for their own sake)
The role is deliberately broad: you won't be confined to a single specialty. You may focus on measurement design, statistical analysis, systems understanding, or result interpretation—but the team collectively owns all aspects with no gaps.
Essential qualifications:
- Strong software engineering experience across multiple projects or systems over several years
- Hands-on experience in Linux-based environments, ideally with distributed or high-performance systems
- Proficiency in Python
- Comfort with uncertainty and evidence; preference for clarity over presentation
- Willingness to challenge conclusions when data is weak
The ideal candidate brings significant practical experience and sound engineering judgment, with depth in at least one area but also the ability to work across boundaries.
