Graduate Machine Learning Engineer, Graphcore
2026, Applied AI
2+ days a week in office
Advanced AI systems and services
Open for applications
Advanced AI systems and services
501-1000 employees
Open for applications
2+ days a week in office
501-1000 employees
Company mission
To make machine learning faster, easier and more intelligent.
Role
Who you are
- 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
- 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
- Desirable:
Desirable
- Development of deep learning models including large generative models for language, vision and other modalities
- Low-precision/efficient compute
- 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
What the job involves
- 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
- 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
- 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
Our take
The hardware required to train AI models comes at a high cost, both financially and environmentally, producing millions of dollars in expenses and hundreds of tons of greenhouse gases. In response, Graphcore develops accelerator chips designed to expedite AI system training while significantly reducing power consumption.
AI accelerators like those crafted by Graphcore specialise in enhancing specific AI applications, such as artificial neural networks, deep learning, and machine learning. Utilising multicore designs and prioritising low-precision arithmetic or in-memory computing, these accelerators elevate the performance of large AI algorithms, achieving breakthrough results in natural language processing and computer vision.
Graphcore has ambitious plans to create an AI supercomputer, aiming to be one of the most powerful in its class. However, progress on this has extended beyond its planned 2024 debut, and the company was purchased by Softbank later that year. Though the acquisition by the Japanese multinational lowered Graphcore's valuation and highlighted structural problems in the local financing of UK tech pioneers, the buyout will undoubtedly aid the company in its ambitions to shape the future of AI computing.
Freddie
Company Specialist at Welcome to the Jungle
Insights
Company
Funding (last 2 of 6 rounds)
Dec 2020
$222m
SERIES E
Feb 2020
$150m
SERIES D
Total funding: $682m
Company 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
- Wellbeing: We want to ensure that our employees have the resources and support they need to prioritise their health and wellbeing
- Healthcare: We offer Private Medical Insurance (PMI) to our employees to help them take care of their health and that of their loved ones
- 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
- 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
Company HQ
Old City, Bristol, UK
Leadership
Nigel Toon
(Co-Founder & Exec Chair)Previously the President and CEO at picoChip Designs (processors for telecoms). As well as the VP and GM of Europe at Altera (semiconductors, merged with Intel).
Simon Knowles
(Co-Founder & CTO)Founded two successful semiconductor companies, Element14 and Icera, for a combined value of over $1 billion.
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