At a Glance
- Tasks: Develop and optimise AI models for cutting-edge hardware in a dynamic team.
- Company: Graphcore is at the forefront of AI technology, innovating with specialised hardware.
- Benefits: Enjoy flexible working, generous leave, private medical insurance, and healthy snacks.
- Why this job: Join a collaborative environment to advance AI and make a real impact.
- Qualifications: Bachelor/Master's/PhD in relevant fields; strong skills in Python and deep learning frameworks.
- Other info: Inclusive workplace with support for diverse backgrounds and flexible interview processes.
The predicted salary is between 36000 - 60000 £ per year.
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.
If you’re excited about advancing the next generation of AI models on cutting-edge hardware, we’d love to hear from you!
Responsibilities and Duties- 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 the AI community and keep in touch with the latest developments in AI.
Essential skills:
- 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.
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.
Machine Learning Engineer employer: graphcore
Contact Detail:
graphcore Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer
✨Tip Number 1
Familiarise yourself with Graphcore's technology and products. Understanding how their hardware interacts with AI models will give you an edge in discussions during interviews, showcasing your genuine interest in the company.
✨Tip Number 2
Engage with the AI community by attending relevant conferences or webinars. This not only helps you stay updated on the latest trends but also provides networking opportunities that could lead to referrals or insights about the role.
✨Tip Number 3
Prepare to discuss specific projects where you've implemented machine learning models. Be ready to explain your thought process, the challenges you faced, and how you optimised performance, as this will demonstrate your hands-on experience.
✨Tip Number 4
Showcase your collaboration skills by highlighting experiences where you've worked cross-functionally. Emphasising your ability to communicate complex technical concepts to diverse teams will resonate well with Graphcore's team-oriented culture.
We think you need these skills to ace Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in machine learning, software development, and any specific projects that demonstrate your skills with frameworks like PyTorch or JAX. Use keywords from the job description to align your experience with what Graphcore is looking for.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for AI and your understanding of Graphcore's technology. Mention specific projects or experiences that relate to the responsibilities outlined in the job description, and express your enthusiasm for contributing to their team.
Showcase Your Technical Skills: In your application, include examples of your proficiency in Python and any experience with C++ or other languages. Highlight any projects where you implemented or optimised machine learning models, especially those that involved distributed training or performance optimisation.
Demonstrate Collaboration Experience: Since the role involves working closely with various teams, provide examples of past collaborative projects. Emphasise your ability to communicate complex technical concepts clearly and how you've successfully worked in cross-functional environments.
How to prepare for a job interview at graphcore
✨Showcase Your Technical Skills
Be prepared to discuss your experience with deep learning frameworks like PyTorch or JAX. Highlight specific projects where you've implemented and optimised machine learning models, as this will demonstrate your technical proficiency.
✨Understand Graphcore's Technology
Research Graphcore’s hardware and how it integrates with AI models. Being able to articulate how your skills can enhance their technology will show your genuine interest in the role and the company.
✨Prepare for Problem-Solving Questions
Expect questions that assess your ability to identify performance bottlenecks and improve model efficiency. Practice explaining your thought process clearly, as strong communication is key in cross-functional teams.
✨Engage with the AI Community
Demonstrate your passion for AI by discussing any contributions to open-source projects or relevant research papers. This shows that you are proactive and engaged with the latest developments in the field.