Graduate Machine Learning Engineer, Graphcore
2026, Applied AI
2+ days a week in office
Advanced AI systems and services
Be an early applicant
Advanced AI systems and services
201-500 employees
Be an early applicant
2+ days a week in office
201-500 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
Traditional hardware, mainly GPUs, used for training these 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 is developing 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 creat an AI supercomputer set to debut in 2024, aiming to be one of the most powerful in its class. The company is actively seeking strategic partnerships to bring this vision to fruition, signaling its commitment to shaping 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
(CEO)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
(CTO)Founded two successful semiconductor companies, Element14 and Icera, for a combined value of over $1 billion.
Salary benchmarks
We don't have enough data yet to provide salary benchmarks for this role.
Submit your salary to help other candidates with crowdsourced salary estimates.
Share this job
View 31 more jobs at Graphcore