Senior Machine Learning Engineer, Graphcore
Large Systems
More information about location
2+ days a week in office
Advanced AI systems and services
Be an early applicant
Advanced AI systems and services
501-1000 employees
Be an early applicant
More information about location
2+ days a week in office
501-1000 employees
Company mission
To make machine learning faster, easier and more intelligent.
Role
Who you are
- If you're excited about advancing the next generation of AI models on cutting-edge hardware, we’d love to hear from you!
- 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 or C++ software development skills
- Expertise in deep learning from model training to optimisation and evaluation
- Experience in distributed training or inference of ML models across 64+ accelerators
- Capable of designing, executing and reporting from ML experiments
- Developed deep understanding of performance bottlenecks and how to overcome them
- 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:
- MLOps for Kubernetes-based clusters
- Building production systems with large language models
- Efficient computing based on low-precision arithmetic
- Experience writing C++/Triton/CUDA kernels for performance optimisation of ML models
- Familiarity with HPC systems and networking including Infiniband, NVLink, RoCE technologies
- 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 Senior 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
- You will work on large scale systems where performance is critical to the success of our projects
- 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
- We seek engineers with strong technical skills and an understanding of AI model implementation at scale, 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 and at scale
- 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 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 necessary 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
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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