Machine Learning Engineer, Graphcore
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
- Bachelor/Master's/PhD or equivalent experience in Machine Learning, Computer Science, Maths, Data Science, or related field
- Proficiency in deep learning frameworks like PyTorch/JAX
- Strong Python software development skills (nice to have C++/other languages)
- Familiar with deep learning fundamentals: models, optimisation, evaluation and scaling
- Capable of designing, executing and reporting from ML experiments
- Ability to move quickly in a dynamic environment
- Enjoy cross-functional work collaborating with other teams
- Strong communicator - able to explain complex technical concepts to different audiences
Desirable
- Experience in one or more of: {distributed training of large-scale ML models, building production systems with large language models, efficient computing based on low-precision arithmetic, deep learning models including large generative models for language, vision and other modalities}
- Experience writing C++/Triton/CUDA kernels for performance optimisation of ML models
- Have contributed to open-source projects or published research papers in relevant fields
- Knowledge of cloud computing platforms
- Keen to present, publish and deliver talks in the AI community
What the job involves
- As a 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 finding opportunities to innovate and differentiate Graphcore’s technology
- We seek engineers with strong technical skills and an understanding of AI model implementation, eager to make a tangible impact in this rapidly evolving field
- The Applied AI team’s role is to be proxies for our customers, we need 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 multiple modalities and applications
- Implement the latest machine learning models and optimise them for performance and accuracy, scaling to 1000s of accelerators
- Test and evaluate new internal software releases, provide feedback to software engineering teams, make vital code fixes, and conduct code reviews
- Benchmark models and key ML techniques to identify performance bottlenecks and improve model efficiency
- Design and conduct experiments on novel AI methods, implement them and evaluate results
- Collaborate with Research, Software, and Product teams to define, build, and test Graphcore’s next generation of AI hardware
- Engage with AI community and keep in touch with the latest developments in AI
Salary benchmarks
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
Articles
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.
People progressing
Joined Graphcore as a Test Technician in 2021 and has since been promoted to Semiconductor Test Engineer in 2019 and Staff SIlicon Test Engineer in 2023.
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