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For Companies At a Glance
- Tasks: Develop and optimise cutting-edge AI models for advanced technology.
- Company: Join Graphcore, a leader in AI innovation and part of the SoftBank Group.
- Benefits: Enjoy flexible working, competitive salary, generous leave, and health plans.
- Why this job: Make a real impact in AI while collaborating with top experts in the field.
- Qualifications: Degree in Machine Learning or related field; experience with deep learning frameworks.
- Other info: Inclusive culture with excellent career growth and opportunities to engage in the AI community.
The predicted salary is between 36000 - 60000 ÂŁ per year.
Cambridge, UK
About us
Graphcore is one of the world’s leading innovators in Artificial Intelligence compute. It is developing hardware, software and systems infrastructure that will unlock the next generation of AI breakthroughs and power the widespread adoption of AI solutions across every industry. As part of the SoftBank Group, Graphcore is a member of an elite family of companies responsible for some of the world’s most transformative technologies. Together, they share a bold vision: to enable Artificial Super Intelligence and ensure its benefits are accessible to everyone. Graphcore’s teams are drawn from diverse backgrounds and bring a broad range of skills and perspectives. A melting pot of AI research specialists, silicon designers, software engineers and systems architects, Graphcore enjoys a culture of continuous learning and constant innovation.
Job Summary
As a Graduate Machine Learning Engineer in the Applied AI team at Graphcore, you will contribute to advancing AI technology by developing and optimising AI models tailored to our specialised hardware. Working closely with the Software development and Research teams, you will play a critical role in identifying opportunities to innovate and differentiate Graphcore’s technology. This role is ideal for someone who loves working hands‑on with models, has a strong foundation in ML fundamentals, and wants to push the boundaries of AI performance in real‑world systems.
The Team
The Applied AI team’s role is to understand the latest AI models, applications, and software to ensure that Graphcore’s technology works seamlessly with the AI ecosystem. We build reference applications, contribute to key software libraries e.g. optimising kernels for efficiency on our hardware, and collaborate with the Research team to develop and publish novel ideas in domains such as efficient compute, model scaling and distributed training and inference of AI models for different modalities and applications.
Responsibilities and Duties
- Implement state‑of‑the‑art machine learning models and optimise them for performance and accuracy, scaling to 1000s of accelerators.
- Evaluate new software releases, provide feedback to software engineering teams, make necessary code fixes, and conduct code reviews.
- Benchmark models and key model components to identify performance bottlenecks and improve model efficiency.
- Design and conduct experiments on novel AI methods, analyse and report results clearly.
- Collaborate with Research, Software, and Product teams to define, build, and test Graphcore’s next generation of AI hardware.
- Stay current with AI research and actively engage with the broader AI and open‑source community.
Candidate Profile
- Bachelor’s/Master's degree in Machine Learning, Computer Science, Maths, Data Science, or related field.
- Proficiency in deep learning frameworks such as PyTorch/JAX and strong software development skills.
- Solid understanding of deep learning fundamentals — architectures, optimisation, evaluation, and scaling.
- Capable of designing, executing and reporting from ML experiments.
- Comfortable working in a fast‑moving, occasionally ambiguous environment.
- Enjoy cross‑functional work collaborating with other teams.
Experience in one or more of:
- Development of deep learning models including large generative models for language, vision and other modalities;
- Distributed training of large‑scale ML models.
- Experience writing high performance C++/Triton/CUDA kernels.
- Contributions to open‑source projects or published research.
- Familiarity with cloud platforms and ML infrastructure.
- Enthusiasm for presenting, publishing, or engaging in the AI community.
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.
2026 Graduate Machine Learning Engineer - Applied AI in Cambridge employer: graphcore
Contact Detail:
graphcore Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land 2026 Graduate Machine Learning Engineer - Applied AI in Cambridge
✨Tip Number 1
Network like a pro! Reach out to current employees at Graphcore on LinkedIn or attend AI meetups. A friendly chat can give you insider info and maybe even a referral!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects. Whether it's a GitHub repo or a personal website, let your work speak for itself.
✨Tip Number 3
Prepare for the interview by brushing up on ML fundamentals and recent advancements in AI. Be ready to discuss how you can contribute to Graphcore's innovative projects.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you're genuinely interested in joining the Graphcore team.
We think you need these skills to ace 2026 Graduate Machine Learning Engineer - Applied AI in Cambridge
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that align with the Graduate Machine Learning Engineer role. Highlight any relevant projects or coursework in machine learning, especially those involving deep learning frameworks like PyTorch or JAX.
Craft a Compelling Cover Letter: Use your cover letter to tell us why you're passionate about AI and how your background makes you a great fit for Graphcore. Share specific examples of your work with AI models and your enthusiasm for pushing the boundaries of technology.
Showcase Your Projects: If you've worked on any interesting AI projects, whether in school or as personal endeavours, make sure to mention them. We love seeing hands-on experience, especially if it involves optimising models or contributing to open-source projects.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands and shows us you're serious about joining the Graphcore team!
How to prepare for a job interview at graphcore
✨Know Your ML Fundamentals
Brush up on your machine learning fundamentals, especially deep learning architectures and optimisation techniques. Be ready to discuss how these concepts apply to real-world scenarios, particularly in relation to Graphcore's technology.
✨Showcase Your Hands-On Experience
Prepare to talk about any hands-on projects you've worked on, especially those involving deep learning frameworks like PyTorch or JAX. Highlight specific challenges you faced and how you overcame them, as this will demonstrate your problem-solving skills.
✨Engage with the AI Community
Familiarise yourself with recent advancements in AI and be prepared to discuss them. Mention any contributions you've made to open-source projects or research publications, as this shows your passion for the field and commitment to continuous learning.
✨Collaborative Mindset
Since the role involves cross-functional collaboration, think of examples where you've successfully worked with diverse teams. Emphasise your ability to communicate effectively and adapt to different working styles, which is crucial in a fast-moving environment like Graphcore.
