Machine Learning Engineer (Large Systems)
Machine Learning Engineer (Large Systems)

Machine Learning Engineer (Large Systems)

Bristol Full-Time 36000 - 60000 £ / year (est.) No home office possible
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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: Make a tangible impact in AI while collaborating with top experts in a vibrant culture.
  • Qualifications: Bachelor/Master's/PhD in relevant fields; strong skills in Python and deep learning frameworks.
  • Other info: Inclusive work environment 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 Team

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 AI community and keep in touch with the latest developments in AI.

Candidate Profile

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.

Benefits

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.

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Machine Learning Engineer (Large Systems) employer: Cerebras

Graphcore is an exceptional employer for Machine Learning Engineers, offering a dynamic work environment in the heart of Bristol where innovation thrives. With a strong commitment to employee growth, flexible working arrangements, and a comprehensive benefits package including private medical insurance and generous parental leave, Graphcore fosters a culture of inclusivity and collaboration. Join us to make a tangible impact in the rapidly evolving field of AI technology while enjoying a vibrant workplace with healthy snacks and a barista bar.
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Contact Detail:

Cerebras Recruiting Team

StudySmarter Expert Advice 🤫

We think this is how you could land Machine Learning Engineer (Large Systems)

✨Tip Number 1

Familiarise yourself with Graphcore's technology and products. Understanding their hardware and how it integrates with AI models will give you a significant edge during discussions and interviews.

✨Tip Number 2

Engage with the AI community by attending relevant conferences, webinars, or meetups. Networking with professionals in the field can provide insights into current trends and may even lead to referrals.

✨Tip Number 3

Showcase your experience with deep learning frameworks like PyTorch or JAX through personal projects or contributions to open-source. This practical demonstration of your skills can set you apart from other candidates.

✨Tip Number 4

Prepare to discuss your approach to optimising machine learning models. Be ready to share specific examples of how you've identified performance bottlenecks and improved model efficiency in past projects.

We think you need these skills to ace Machine Learning Engineer (Large Systems)

Proficiency in deep learning frameworks (e.g., PyTorch, JAX)
Strong Python software development skills
Understanding of deep learning fundamentals (models, optimisation, evaluation, scaling)
Experience with distributed training of large-scale ML models
Knowledge of performance optimisation techniques (e.g., low-precision arithmetic)
Ability to design, execute, and report on ML experiments
Familiarity with C++/Triton/CUDA for kernel optimisation
Experience in building production systems with large language models
Strong communication skills for explaining complex technical concepts
Ability to work collaboratively across cross-functional teams
Engagement with the AI community and awareness of latest developments
Experience contributing to open-source projects or publishing research papers
Knowledge of cloud computing platforms
Adaptability in a dynamic environment

Some tips for your application 🫡

Understand the Role: Before applying, make sure you fully understand the responsibilities and requirements of the Machine Learning Engineer position at Graphcore. Familiarise yourself with their technology and the specific skills they are looking for.

Tailor Your CV: Customise your CV to highlight relevant experience in machine learning, software development, and any specific frameworks like PyTorch or JAX. Emphasise your technical skills and any projects that demonstrate your ability to optimise AI models.

Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for AI and your understanding of Graphcore's mission. Mention specific projects or experiences that align with the role and express your eagerness to contribute to their team.

Showcase Your Communication Skills: Since strong communication is essential for this role, consider including examples in your application that demonstrate your ability to explain complex concepts clearly. This could be through previous work experiences, presentations, or publications.

How to prepare for a job interview at Cerebras

✨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 and relevance to the role.

✨Understand Graphcore's Technology

Research Graphcore’s hardware and how it integrates with AI models. Familiarise yourself with their unique selling points and be ready to discuss how you can contribute to optimising AI models for their systems.

✨Prepare for Collaborative Scenarios

Since the role involves cross-functional collaboration, think of examples where you've successfully worked with different teams. Be ready to explain how you communicate complex technical concepts to non-technical audiences.

✨Stay Updated on AI Developments

Engage with the latest trends in AI and be prepared to discuss recent advancements or research that excites you. This shows your passion for the field and your commitment to continuous learning, which is crucial for a rapidly evolving area like AI.

Machine Learning Engineer (Large Systems)
Cerebras

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  • Machine Learning Engineer (Large Systems)

    Bristol
    Full-Time
    36000 - 60000 £ / year (est.)

    Application deadline: 2027-09-08

  • C

    Cerebras

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