Location: Bristol, UK
Start date: September 2027
Application deadline: Midnight, 29th November 2026
We encourage you to apply early, as we may close applications before the advertised deadline.

The Graduate Programme at Graphcore

Join Graphcore in a permanent role and start your career with two years of hands-on learning. From day one, you’ll be trusted to take ownership, share your ideas and help shape what we’re creating. You’ll tackle challenging problems alongside a team who support your development and are as excited about your success as you are.

What you’ll work on

Use your programming skills and curiosity to help AI models run efficiently on next-generation Graphcore hardware.

In AI Compute Libraries & Performance, you’ll contribute to C++ libraries and compute kernels for machine learning workloads. Your work will include:

Developing compute kernels: Implement and improve operations such as matrix multiplication (GEMM), convolutions, reductions, elementwise operations and fused kernels that combine multiple operations.
Understanding and improving performance: Use profiling and microbenchmarks to measure execution speed and investigate how threading, cache locality, memory layout and kernel launch efficiency affect performance.
Checking correctness and reliability: Build regression tests and numerical checks, investigate issues and improve the quality of the libraries our customers use.

Through these projects, you’ll develop practical experience connecting numerical computing with how code runs on hardware, helping deliver software that is fast, accurate and reliable.

Your team and support

You’ll join a team building high-performance compute libraries that help customers get more from Graphcore hardware. Our work brings together machine learning, numerical computing and accelerator performance, with colleagues sharing their expertise to solve engineering problems.

You’ll contribute to clearly scoped projects and build your skills with guidance from engineers working in performance and low-level software. Together, we use profiling, experiments and technical reviews to compare approaches, understand trade-offs and improve our solutions.

Your buddy will help you settle in, get familiar with our tools and ways of working, and find the right people to ask. Your manager will meet with you regularly to agree goals, offer feedback and discuss your development, including time for learning.

We welcome different perspectives and make space for everyone to contribute. Questions, alternative approaches and requests for help are part of how we work. As you take on more responsibility, your colleagues will help you work through challenges, and your ideas will be listened to and discussed respectfully.

How you’ll learn and grow

During the two-year programme, you’ll build performance engineering skills through hands-on work on compute kernels, benchmarks and tests.

Profiling and experiments will help you understand where time is spent and how changes to code and data layout affect performance. Numerical validation and regression testing will show you how to check that an optimisation preserves correct results and continues to work as software changes.

Working with specialists and discussing technical trade-offs will help you develop your engineering judgement, explain your findings and connect mathematical operations to efficient implementations on hardware.

What we're looking for

Bachelor’s/Master’s degree in Computer Science, Maths, Machine Learning, Data Science or a related field
Experience with C++11
Familiarity with Python or scripting tools for automation and testing
Understanding of linear algebra, numerical methods or scientific computing, with problem-solving skills and collaborative approach to unfamiliar technical challenges

You can demonstrate these skills through coursework, a dissertation, personal or team projects, research, or other relevant experience. Tell us what you built or investigated, how you approached it and what you learned.