Software Engineer Summer Intern, AI Compute Libraries & Performance 2027

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. 

How you’ll learn and grow

Over the 12 weeks, 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

• Currently studying towards a bachelor’s or master’s degree in Computer Science, Maths, Machine Learning, Data Science or a related field
• Experience with C or C++11, plus familiarity with Python or scripting tools for automation and testing
• An understanding of linear algebra, numerical methods or scientific computing
• Problem-solving skills and collaborative approach to unfamiliar technical challenges

Previous industry experience is not required. Tell us what you built or investigated through coursework, personal or team projects or other relevant experience, how you approached it and what you learned.

Coursework or experience with machine learning frameworks, parallel programming, code optimisation, maths libraries or performance analysis would be useful. These are additional strengths, rather than essential requirements.

Benefits

• Flexible working: Find a balance between your internship and life outside work
• 5 days of annual leave: Take time to rest and recharge during your 12-week internship
• Life in the office: Get to know your colleagues in spaces designed for collaboration, with free food, a great selection of ice cream and an on-site barista
• Learning and development: Build your skills and explore your interests with access to O’Reilly, our online learning platform

We welcome people from all backgrounds and experiences and are committed to building an inclusive environment where everyone can do their best work.

We’re an equal opportunity employer and recognise that everyone brings different strengths and perspectives. If you need any adjustments during the interview process, just let us know - we’re happy to support you.