At a Glance
- Tasks: Design and maintain robust test infrastructure for a complex ML software stack.
- Company: Join Graphcore, a leader in AI compute backed by SoftBank Group.
- Benefits: Flexible working, generous leave, private medical insurance, and a vibrant office culture.
- Other info: Inclusive environment with opportunities for mentorship and career growth.
- Why this job: Make a real impact on the future of AI while working with cutting-edge technology.
- Qualifications: Experience in software engineering, strong Python skills, and familiarity with CI/CD systems.
The predicted salary is between 60000 - 80000 £ per year.
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 datacentre 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
Applicants for this role should have strong experience designing, developing, and maintaining high-quality software systems. The role focuses on testing and validating a complex machine learning software stack, with particular emphasis on software architecture, automation, and engineering best practices. The ideal candidate is an experienced software engineer who values code quality, testability, and long-term maintainability, and enjoys building systems that other engineers rely on. This person will be comfortable working across large codebases, contributing to CI/CD infrastructure, and shaping technical direction through thoughtful design and mentoring in a technically demanding environment spanning ML frameworks, infrastructure, and AI accelerator hardware.
The Team
The ML QA team is composed of highly skilled software engineers with a strong focus on automation, software quality, and data-driven validation. The team works closely with industry-standard machine learning frameworks and models, contributing to upstream open-source projects and collaborating across the wider software organization. Operating in a fast-paced environment, the team plays a critical role in ensuring reliability, performance, and maintainability across the ML software stack, helping to deliver robust and high-quality products to customers.
Responsibilities and Duties
- 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.
- Create representative ML workloads and gain insights from their execution (Numerical accuracy, performance analysis and benchmarking).
- Work closely with all Software development teams, supporting a culture of quality, security and maintainability.
- Review code and designs, setting a high bar for software engineering best practices.
- Mentor and support junior engineers, helping raise the overall technical capability of the team.
- Evaluate existing test strategies and infrastructure, identifying gaps and driving improvements aligned with team and organizational goals.
Candidate Profile
- 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.
- Solid experience with CI/CD systems and automated testing (preferably GitHub-based workflows).
- Experience working in Linux environments.
- Familiarity with C or C++, with the ability to read, debug, and reason about low-level code when needed.
- Proven ability to mentor junior engineers and influence engineering practices within a team.
- Strong problem-solving skills and a proactive, self-directed approach to work.
- Bachelor/Master's/PhD or equivalent experience in Computer Science, Maths, Machine Learning, Data Science, or related field.
- Exposure to machine learning frameworks such as PyTorch, JAX, Triton, TensorFlow.
- Experience with distributed workload management systems such as Kubernetes, VLLM, Keras or MLOps pipelines.
- Experience working with hardware simulators or emulators (e.g. QEMU).
- Experience developing for or working with FPGA-based systems.
- Experience with people management or mentoring.
In addition to a competitive salary, Graphcore offers flexible working, a generous annual leave policy, private medical insurance and health cash plan, a dental plan, pension (matched up to 5%), life assurance and income protection. We have a generous parental leave policy and an employee assistance programme (which includes health, mental wellbeing, and bereavement support). We offer a range of healthy food and snacks at our central Bristol office and have our own barista bar! We welcome people of different backgrounds and experiences; we’re committed to building an inclusive work environment that makes Graphcore a great home for everyone. We offer an equal opportunity process and understand that there are visible and invisible differences in all of us. We can provide a flexible approach to interview and encourage you to chat to us if you require any reasonable adjustments. Applicants for this position must hold the right to work in the UK. Unfortunately at this time, we are unable to provide visa sponsorship or support for visa applications.
Senior ML QA Engineer – Automate & Validate AI Stack employer: graphcore
Graphcore is an exceptional employer, offering a dynamic work environment where innovation thrives and every team member can make a significant impact on the future of AI technology. With a strong focus on employee growth, we provide extensive mentoring opportunities, flexible working arrangements, and a comprehensive benefits package that includes private medical insurance, generous leave policies, and a vibrant office culture in central Bristol, complete with healthy snacks and a barista bar. Join us to be part of a diverse team dedicated to pushing the boundaries of AI compute while enjoying a supportive and inclusive workplace.
StudySmarter Expert Advice🤫
We think this is how you could land Senior ML QA Engineer – Automate & Validate AI Stack
✨Tip Number 1
Network like a pro! Reach out to current employees at Graphcore on LinkedIn or other platforms. A friendly chat can give you insider info and might even lead to a referral, which can boost your chances of landing that Senior ML QA Engineer role.
✨Tip Number 2
Prepare for the interview by brushing up on your Python skills and understanding CI/CD systems. Be ready to discuss your experience with automation and testing frameworks, as these are key for the role. We want to see how you can contribute to our ML software stack!
✨Tip Number 3
Showcase your problem-solving skills during interviews. Think of examples where you've tackled complex issues in software engineering. We love candidates who can think on their feet and bring innovative solutions to the table!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining the Graphcore team and contributing to our exciting AI projects.
We think you need these skills to ace Senior ML QA Engineer – Automate & Validate AI Stack
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the role of Senior ML QA Engineer. Highlight your experience with software design, automation, and testing frameworks that align with what we’re looking for at Graphcore.
Showcase Your Projects:Include specific projects where you've designed or maintained test infrastructures or CI/CD pipelines. We love seeing real examples of your work, especially if they relate to machine learning or complex systems.
Be Clear and Concise:When writing your application, keep it clear and concise. Use bullet points for easy reading and make sure to emphasise your key skills and experiences that match the job description.
Apply Through Our Website:Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. We can’t wait to see what you bring to the table!
How to prepare for a job interview at graphcore
✨Know Your Tech Stack
Make sure you’re well-versed in the technologies mentioned in the job description, especially Python and CI/CD systems. Brush up on your knowledge of machine learning frameworks like PyTorch and TensorFlow, as well as any experience with hardware simulators or emulators. This will show that you’re not just a fit for the role but also genuinely interested in the tech they work with.
✨Showcase Your Problem-Solving Skills
Prepare to discuss specific challenges you've faced in previous roles, particularly those related to software quality and automation. Use the STAR method (Situation, Task, Action, Result) to structure your answers, highlighting how you approached complex problems and what impact your solutions had on the team or project.
✨Emphasise Collaboration and Mentorship
Since the role involves working closely with other teams and mentoring junior engineers, be ready to share examples of how you’ve collaborated in the past. Talk about your approach to fostering a culture of quality and how you’ve helped others grow in their roles. This will demonstrate your ability to contribute positively to the team dynamic.
✨Ask Insightful Questions
Prepare thoughtful questions about the team’s current projects, challenges they face, and their approach to software quality. This not only shows your interest in the role but also gives you a chance to assess if the company culture aligns with your values. It’s a great way to engage with your interviewers and leave a lasting impression.