Enable job alerts via email!

Machine Learning Engineer (Large Systems)

Graphcore

Cambridge

On-site

GBP 125,000 - 150,000

Full time

19 days ago

Job summary

A cutting-edge AI technology company in Cambridge is seeking a Machine Learning Engineer to develop and optimise AI models on their specialised hardware. The role involves testing new software, collaborating with various teams, and engaging with the AI community. Applicants should have a strong background in machine learning, with proficiency in tools like PyTorch and Python. This position includes benefits such as flexible working hours, health insurance, and an inclusive work culture.

Benefits

Flexible working
Private medical insurance
Generous annual leave policy
Health cash plan
Dental plan
Pension (matched up to 5%)
Life assurance
Income protection
Parental leave policy

Qualifications

  • Proficiency in deep learning frameworks like PyTorch/JAX.
  • Strong Python or C++ software development skills.
  • Experience in distributed training or inference of ML models across 64+ accelerators.

Responsibilities

  • Implement latest machine learning models and optimise them.
  • Test and evaluate new internal software releases.
  • Collaborate with Research, Software, and Product teams.

Skills

Deep learning frameworks (PyTorch/JAX)
Python software development
C++ software development
Distributed training of ML models
Communication of technical concepts

Education

Bachelor/Master's/PhD in Machine Learning, Computer Science, Maths or Data Science

Tools

Kubernetes
C++
Job description
Job Summary

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. 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 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 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 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.

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 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.
Candidate Profile
Essential:
  • 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
  • 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.
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.

Applicants for this position must hold the right to work in the UK. Unfortunately at this time, we are unable to provide visa sponsorship or support for visa applications
Get your free, confidential resume review.
or drag and drop a PDF, DOC, DOCX, ODT, or PAGES file up to 5MB.