At a Glance
- Tasks: Benchmark and validate ML models, ensuring performance and reliability across various environments.
- Company: Join Graphcore, a leader in AI compute backed by SoftBank, shaping the future of technology.
- Benefits: Enjoy flexible working, generous leave, private medical insurance, and a vibrant office culture.
- Other info: Inclusive workplace committed to diversity, with excellent career growth opportunities.
- Why this job: Make a real impact in AI while collaborating with top minds in a dynamic environment.
- Qualifications: Experience in ML systems, strong Python skills, and a solid understanding of AI concepts required.
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 working with machine learning systems and frameworks, along with a solid understanding of core AI concepts and model behaviour. The role centres on testing, validating, and benchmarking a complex ML software stack, with a particular focus on performance, reliability, and correctness across modern AI workloads. The ideal candidate is an experienced ML engineer who understands how contemporary models are trained and executed, and who has hands‑on experience debugging functional and performance issues in ML systems. This person will be comfortable working with industry‑standard frameworks and state‑of‑the‑art models, bringing them up on internal infrastructure, and collaborating closely with software and hardware teams in a technically demanding environment spanning ML frameworks, infrastructure, and AI accelerator hardware.
The Team
The MLQA 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
- Benchmark ML models and frameworks, analysing results to identify regressions, performance bottlenecks, and correctness issues.
- Work hands‑on with industry‑standard ML frameworks to validate functionality and performance across different execution environments.
- Build and maintain automated testing and benchmarking pipelines targeting simulators, emulators, and physical hardware.
- Collaborate closely with software teams to ensure adequate test coverage for new and existing features.
- Develop tooling and scripts (primarily in Python) to support testing, benchmarking, and functional reporting.
- Take ownership over aspects of our testing and infrastructure, owning the roadmap and driving innovation independently.
Candidate Profile
- Experience working in Machine Learning or ML‑adjacent engineering roles.
- Strong foundation in core AI and ML concepts (e.g. neural networks, training vs inference, numerical precision, performance trade‑offs).
- Hands‑on experience with one or more major ML frameworks such as PyTorch, TensorFlow, JAX, or similar.
- Strong proficiency in Python for ML workflows, experimentation, and automation.
- Experience designing, running, and analysing ML benchmarks or experiments.
- Experience working in Linux environments.
- Strong analytical and debugging skills, with the ability to reason about model behaviour and system performance.
- Bachelor/Master’s/PhD or equivalent experience in Computer Science, Maths, Machine Learning, Data Science, or related field.
- Experience with MLOps pipelines, model deployment, or production ML systems.
- Familiarity with performance analysis, profiling tools, or numerical accuracy validation.
- Exposure to distributed training or inference systems.
- Experience with hardware‑accelerated ML, compilers, or system‑level performance considerations.
- Familiarity with CI/CD systems used for ML workflows.
- Experience contributing to open‑source ML frameworks or tooling.
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. We take pride in our commitment to creating an inclusive and diverse workplace. As part of our recruitment process, we ask for confidential diversity data from all applicants. This data will be anonymised so that no personal identification information will be collected, and is retained for statistical purposes only and is not attached to your application. Your responses to the following three questions will remain confidential and will not impact or be used in any way in regards to your application. We are only using this data to improve our hiring process to be inclusive of all diversity backgrounds.
Staff Engineer (ML Engineer) Cambridge, UK employer: graphcore
Graphcore is an exceptional employer, offering a dynamic work environment in Cambridge where innovation thrives. With a strong focus on employee growth, we provide opportunities to work alongside industry experts in AI and machine learning, while enjoying benefits such as flexible working, comprehensive health plans, and a commitment to inclusivity. Our culture encourages collaboration and creativity, making it a rewarding place for those looking to make a significant impact in the fast-evolving field of AI technology.
StudySmarter Expert Advice🤫
We think this is how you could land Staff Engineer (ML Engineer) Cambridge, UK
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with Graphcore employees on LinkedIn. A friendly chat can sometimes lead to job opportunities that aren't even advertised!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your ML projects, experiments, or contributions to open-source frameworks. This gives you a chance to demonstrate your expertise and passion for AI directly.
✨Tip Number 3
Prepare for technical interviews by brushing up on your ML concepts and debugging skills. Practice coding challenges related to Python and ML frameworks, as this will help you feel more confident when it’s time to shine.
✨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.
We think you need these skills to ace Staff Engineer (ML Engineer) Cambridge, UK
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Staff Engineer role. Highlight your experience with machine learning systems and frameworks, and don’t forget to mention any hands-on work you've done with industry-standard tools like PyTorch or TensorFlow.
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about AI and how your skills align with our mission at Graphcore. Be genuine and let your personality come through!
Showcase Your Projects:If you’ve worked on any relevant projects, make sure to include them in your application. Whether it's a personal project or something from your previous job, showcasing your hands-on experience can really set you apart.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re keen on joining our team!
How to prepare for a job interview at graphcore
✨Know Your ML Frameworks
Make sure you’re well-versed in the major ML frameworks like PyTorch and TensorFlow. Brush up on their functionalities, strengths, and weaknesses, as you might be asked to discuss how they relate to the role and your past experiences.
✨Showcase Your Debugging Skills
Prepare to talk about specific instances where you’ve debugged performance issues or model behaviour in ML systems. Have examples ready that demonstrate your analytical skills and how you approached solving complex problems.
✨Understand the AI Compute Stack
Familiarise yourself with the complete AI compute stack, from silicon to software. Being able to discuss how different components interact will show your depth of knowledge and readiness to collaborate with hardware and software teams.
✨Prepare for Technical Questions
Expect technical questions related to benchmarking, testing, and validating ML models. Review key concepts like numerical precision and performance trade-offs, and be ready to explain them clearly and confidently.