At a Glance
- Tasks: Build and validate cutting-edge ML software, creating scalable test frameworks and CI/CD pipelines.
- Company: Join Graphcore, a leader in AI technology with a focus on innovation and collaboration.
- Benefits: Enjoy flexible working hours, private healthcare, pension plans, and a strong focus on wellbeing.
- Other info: Great opportunities for career progression and personal development in a dynamic environment.
- Why this job: Shape the future of AI while mentoring others and solving complex engineering challenges.
- Qualifications: Experience in CI/CD, automated testing, and strong Python skills are essential.
The predicted salary is between 56700 - 69300 £ per year.
Build the software that validates Graphcore’s machine learning stack, from frameworks to AI accelerator hardware. You’ll design production-quality test infrastructure that engineers can extend, maintain and rely on as Graphcore’s ML software grows. Your work will help teams deliver reliable products, with clear evidence of numerical accuracy and performance across the stack. You’ll architect scalable test frameworks and build CI/CD pipelines that run across simulators, emulators and physical hardware. You’ll create representative ML workloads, analyse their execution and turn the results into practical improvements to testing and tooling. This role gives you scope to shape software architecture and developer experience, not just add tests to existing systems. You’ll deepen your understanding of the AI compute stack while solving engineering problems that connect software design with hardware behaviour.
Graphcore’s ML QA engineers build automation, work with industry-standard frameworks and models, and contribute to upstream open-source projects. You’ll work across software teams, using code and design reviews to strengthen quality, security and maintainability. You’ll use workload results to challenge assumptions and guide technical decisions about how Graphcore tests its ML stack. You’ll own improvements, act quickly on testing gaps and mentor junior engineers to raise the team’s technical standards.
Benefits
- Work-Life Balance: We believe you should have a good work-life balance. Everyone’s situation is different, so we offer flexible working arrangements and start and finish times, allowing you to choose when to work from home or come into the office.
- Healthcare: We offer Private Medical Insurance (PMI) to our employees to help them take care of their health and that of their loved ones.
- Pension: We believe that our employees deserve the best when it comes to planning for their future.
- Income Protection: Graphcore understands that unforeseen circumstances can happen, and we want to ensure our employees have the necessary financial security in place.
- Progression: We prioritise learning and development as an integral part of our culture at Graphcore. Our Talent Development team is dedicated to equipping all our employees with the necessary tools, resources, and support to enhance their skills and reach their full potential in their careers.
- Wellbeing: We want to ensure that our employees have the resources and support they need to prioritise their health and wellbeing.
While we have outlined a set of requirements, we value transferable skills and diverse experiences. We also welcome engineers returning to the profession after a career break, including through returnship routes.
A self-directed approach to solving complex problems, identifying gaps and driving improvements. Experience building CI/CD pipelines and automated testing systems in Linux environments. Familiarity with C or C++, including the ability to read, debug and reason about low-level code. Strong Python skills, demonstrated through designing, developing and maintaining production-quality software. The ability to mentor junior engineers and influence engineering practices through constructive feedback. Software design and architecture skills for large, complex codebases, with a focus on testability and long-term maintainability.
Staff Software Engineer (ML Quality Assurance) in Cambridge employer: graphcore
Graphcore is an exceptional employer, offering a vibrant work culture that fosters continuous learning and innovation in the heart of London. With a commitment to inclusivity and employee well-being, we provide flexible working arrangements, comprehensive benefits including private medical insurance and generous parental leave, and opportunities for professional growth within a cutting-edge AI environment. Join us to be part of a transformative journey in AI technology, where your contributions will directly impact the future of intelligent computing.
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We think this is how you could land Staff Software Engineer (ML Quality Assurance) in Cambridge
✨Join Local Tech Meetups
Get out there and mingle with fellow developers by joining local tech meetups. It’s a fantastic way to meet people who might be working at graphcore or know someone who does. Plus, you can pick up some trendy tech skills and trends while you're at it!
✨Contribute to Open Source Projects
Show off your coding chops by jumping into open-source projects. Not only does this give you practical experience, but it also gets you noticed in the dev community. You'll create a killer portfolio that speaks volumes about your skills to graphcore.
✨Tap into Online Developer Communities
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We think you need these skills to ace Staff Software Engineer (ML Quality Assurance) in Cambridge
Some tips for your application 🫡
Show off your coding skills:When applying for a software engineering role, it's super important to showcase your coding skills. Make sure your CV includes your tech stack, any relevant programming languages you’re comfortable with, and examples of projects you've worked on. If you have a GitHub profile, link it up! We love to see code in action.
Tailor your portfolio:For a full-time role, we’d expect to see some solid examples of your work in your portfolio. Make sure to include at least two or three projects that highlight your problem-solving skills and your ability to work with different technologies. Focus on the projects that are most relevant to the position at graphcore.
Craft a killer cover letter:Your cover letter is your chance to stand out—make it personal! Explain why you want to work at graphcore and how your skills align with the role. Show us your passion for software development. We dig enthusiastic candidates who understand the value of collaboration and continuous learning!
Be clear and concise:When it comes to writing your CV and cover letter, clarity is key. Avoid jargon that could confuse us and stick to simple, direct language. Highlight your achievements with quantifiable results where possible, and keep everything easy to read. A well-organised application goes a long way!
How to prepare for a job interview at graphcore
✨Brush Up on Your Coding Skills
For a full-time software engineering role, it's crucial that we stay sharp with our coding abilities. Expect technical questions that might involve solving problems on the spot or discussing algorithms. Practise on platforms like LeetCode or HackerRank to get comfortable with the types of questions that often come up.
✨Know Your Tools and Frameworks
Make sure we’re well-acquainted with the tools and technologies listed in the job description. Familiarise ourselves with any specific frameworks or programming languages mentioned. If graphcore uses React or Node.js, for instance, be ready to discuss how we’ve used them in previous projects or coursework.
✨Showcase Your Projects
Bring along a portfolio that highlights our best work. This could be code samples, GitHub repositories, or any side projects we’ve built. Make sure we can talk through our thought process for each project, especially the challenges we faced and how we solved them—this shows our problem-solving skills in action.
✨Prepare for Behavioural Questions
While technical skills are key, full-time positions also require cultural fit. Be ready to discuss our previous experiences and how we handle teamwork, conflict, and deadlines. Brush up on the STAR method—Situation, Task, Action, Result—to clearly articulate our past experiences when discussing how we've contributed to a team.