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Staff Software Engineer (ML QA)
Graphcore · Cambridge, United Kingdom
About The Role
Join Graphcore, a leading AI hardware and software company, as a Staff Software Engineer (ML QA). In this role, you will design, implement, and maintain robust test infrastructure and automation for a complex machine learning software stack. You will work closely with all software development teams, review code and designs, mentor junior engineers, and evaluate existing test strategies and infrastructure. The ideal candidate will have strong experience in software design and architecture, proficiency in Python, and experience with CI/CD systems and automated testing.
- Design, implement, and maintain robust test infrastructure and automation for a complex ML software stack.
- Architect and evolve test frameworks and tooling with a focus on scalability, maintainability, and developer experience.
- Build and maintain CI/CD pipelines targeting simulators, emulators (e.g. QEMU), and physical hardware.
- Proven ability to mentor junior engineers and influence engineering practices within a team
- Experience working in Linux environments
- Strong software design and architecture skills, with experience working on large or complex systems
- Strong proficiency in Python, including experience building and maintaining production codebases
- Bachelor/Master's/PhD or equivalent experience in Computer Science, Maths, Machine Learning, Data Science, or related field
- Experience in production-quality software engineering roles
- Solid experience with CI/CD systems and automated testing (preferably GitHub-based workflows)
- Familiarity with C or C++, with the ability to read, debug, and reason about low-level code when needed
- Strong problem-solving skills and a proactive, self-directed approach to work
- Experience with people management or mentoring
- Experience developing for or working with FPGA-based systems
- Experience working with hardware simulators or emulators (e.g. QEMU)
- Experience with distributed workload management systems such as Kubernetes, VLLM, Keras or MLOps pipelines
- Exposure to machine learning frameworks such as PyTorch, JAX, Triton, TensorFlow
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