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Staff Software Engineer (ML QA)

Graphcore · London, United Kingdom

External listingfull-time2 months ago

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 engineering, a proactive approach to work, and a passion for quality and maintainability.

  • 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.
  • Bachelor/Master's/PhD or equivalent experience in Computer Science, Maths, Machine Learning, Data Science, or related field
  • Strong problem-solving skills and a proactive, self-directed approach to work
  • Experience working in Linux environments
  • Proven ability to mentor junior engineers and influence engineering practices within a team
  • Experience in production-quality software engineering roles
  • 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
  • Familiarity with C or C++, with the ability to read, debug, and reason about low-level code when needed
  • Solid experience with CI/CD systems and automated testing (preferably GitHub-based workflows)
  • 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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