Senior Systems Engineer – Performance & Reliability (Analysis)
About this role
About Graphcore
At Graphcore, we’re building the future of AI compute.We’re a team of semiconductor, software and AI experts, with deep experience in creating the complete AI compute stack - from silicon and software to infrastructure at datacenter scale.As part of the SoftBank Group, backed by significant long-term investment, we are delivering key technology into the fast-growing SoftBank AI ecosystem.To meet the vast and exciting AI opportunity, Graphcore is expanding its teams around the world.We are bringing together the brightest minds to solve the toughest problems, in a place where everyone has the opportunity to make an impact on the company, our products and the future of artificial intelligence.
Requirements
- Strong software engineering experience, typically gained across multiple projects or systems over several years
- Experience working in Linux-based environments, ideally with distributed or high-performance systems
- Proficiency in Python
- Experience with automation and CI/CD systems (e.g. GitLab CI, Jenkins, GitHub Actions)
- Ability to design, implement, and run experiments or tests that produce meaningful results
- Ability to interpret results and communicate findings clearly, with an emphasis on accuracy and usefulness to decision-making
- Comfortable working in areas where requirements are not fully defined and judgement is required
Nice to have
- Experience working with large-scale or distributed systems (e.g. clusters, cloud platforms, HPC environments)
- Experience with performance, reliability, or systems-level testing/measurement
- Familiarity with pytest or similar frameworks for structured test/measurement execution
- Experience analysing system behaviour under load (compute, network, or ML workloads)
- Experience working with containerisation, orchestration, or provisioning systems (e.g. Docker, Kubernetes, OpenStack)
- Proficiency in other applications programming languages (e.g. C++)7. Exposure to data analysis, statistics, or interpreting variability in results
About Graphcore
At Graphcore, we’re building the future of AI compute.We’re a team of semiconductor, software and AI experts, with deep experience in creating the complete AI compute stack - from silicon and software to infrastructure at datacenter scale.As part of the SoftBank Group, backed by significant long-term investment, we are delivering key technology into the fast-growing SoftBank AI ecosystem.To meet the vast and exciting AI opportunity, Graphcore is expanding its teams around the world.We are bringing together the brightest minds to solve the toughest problems, in a place where everyone has the opportunity to make an impact on the company, our products and the future of artificial intelligence. Job Summary We turn large-scale system measurements into decisions. Our team runs workloads across clusters of machines and collects detailed performance data. The challenge isn’t running the measurements—it’s deciding what they mean, and whether a system is good enough to enter production. You will work with results from real systems and help answer questions like: Is this system behaving as expected? Is performance stable enough to trust? Does this meet the criteria to enter production? This work includes systems engineering and analysis. It involves understanding variability, repeatability, and the differences between signal and noise. You won’t be confined to a single role. You may shape measurements, influence how they are run, or improve how results are interpreted. You are free to specialise, but the team is responsible for leaving no gaps. This is not a dashboarding or reporting role in the traditional sense. The goal is to produce outputs that support real engineering decisions. We’re looking for engineers who: Think carefully about uncertainty and evidence Prefer clarity over presentation Are comfortable challenging conclusions when data is weak Selection criteria: Our engineers typically bring significant practical experience and sound engineering judgement. Depth in one area is valued, but the ability to work across boundaries is equally important.