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2026 Graduate Software Engineer - Kernel Engineering Team

Graphcore

Bristol

On-site

GBP 30,000 - 40,000

Full time

Today
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Job summary

A prominent AI hardware innovator in the UK is looking for a Graduate Software Engineer to contribute to the development of high-performance machine learning kernels. The role involves optimizing compute kernels and requires expertise in C/C++11, as well as strong problem-solving skills. In addition to a competitive salary, this role offers flexible working, generous leave, and various employee benefits to foster an inclusive workplace.

Benefits

Flexible working
Private medical insurance
Parental leave policy

Qualifications

  • Experience with linear algebra, numerical methods, or scientific computing.
  • Ability to work collaboratively in a fast-paced environment.
  • Familiarity with ML frameworks or code optimisation.

Responsibilities

  • Support design and implementation of kernels for linear algebra and tensor ops in C++.
  • Profile and optimise for next-gen AI hardware.
  • Debug issues and improve product quality.

Skills

C/C++11 experience
Problem-solving skills
Performance optimisation

Education

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

Tools

Python
Math libraries (MKL, OpenBLAS)
Job description

Bristol, UK

About Us

Graphcore is one of the world’s leading innovators in Artificial Intelligence compute.

It is developing hardware, software and systems infrastructure that will unlock the next generation of AI breakthroughs and power the widespread adoption of AI solutions across every industry.

As part of the SoftBank Group, Graphcore is a member of an elite family of companies responsible for some of the world’s most transformative technologies. Together, they share a bold vision: to enable Artificial Super Intelligence and ensure its benefits are accessible to everyone.

Graphcore’s teams are drawn from diverse backgrounds and bring a broad range of skills and perspectives. A melting pot of AI research specialists, silicon designers, software engineers and systems architects, Graphcore enjoys a culture of continuous learning and constant innovation.

Job Summary

We are looking for a Graduate Software Engineer to join a team pioneering the development of high-performance machine learning (ML) kernels for a new generation of AI hardware.

In this role, you will contribute to building optimised compute kernels that support a wide range of ML operators—powering applications from convolutional neural networks (CNNs) to large language models (LLMs).

You’ll leverage low-level programming and hardware‑aware optimisation techniques to extract maximum performance and efficiency from modern accelerators. This is a unique opportunity to work at the intersection of ML, numerical computing, and scalable systems.

The Team

This is an exciting opportunity to join an expanding team at Graphcore. The Kernel Engineering team is responsible for delivering high performance compute library to help customers gain the maximum performance from AI hardware.

Responsibilities and Duties
  • Supporting the design and implementation of kernels for linear algebra and tensor ops (GEMM, batched GEMM, convolutions, reductions, elementwise and fused operations) in C++
  • Profile and optimise for the next generation of AI hardware - threading, cache locality, memory layout, and kernel launch efficiency.
  • Support performance and correctness - add microbenchmarks, regression tests, numerics validation
  • Debug issues, resolve bugs and generally improve the quality and functionality of the product
About you

You are open-minded and collaborative with interests in performance optimisation and memory-efficient designs, and you are looking to join a team of experts.

You are comfortable to discuss technical tradeoffs, receive feedback and iterate on solutions and you are drawn to technically challenging problems and use analyticals reasoning to navigate unfamiliar domains.

Qualifications
  • Bachelor or Master's Degree in Computer Science, Maths, Machine Learning, Data Science, or related field
  • Experirence in C/C++11.
  • Familiarity with Python or scripting tools for automation and testing.
  • Understanding of linear algebra, numerical methods, or scientific computing.
  • Good problem-solving skills and ability to work collaboratively in a fast‑paced environment.
Preferred Qualifications
  • Courseworks or past experience in using ML frameworks, parallel programming, or code optimisation.
  • Exposure to math libraries such as MKL or OpenBLAS.
  • Knowledge of performance analysis tools.

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.

We take pride in our commitment to creating an inclusive and diverse workplace. As part of our recruitment process, we ask for confidential diversity data from all applicants. This data will be anonymised so that no personal identification information will be collected, and is retained for statistical purposes only and is not attached to your application. Your responses to the following three questions will remain confidential and will not impact or be used in any way in regards to your application. We are only using this data to improve our hiring process to be inclusive of all diversity backgrounds.

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