Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Collaborate with AI researchers to optimise software performance and develop predictive models.
- Company: Join a non-profit focused on responsible AI development and collaborative engineering.
- Benefits: Competitive salary, pension, professional development, and networking opportunities.
- Why this job: Make a real impact in AI while working in a vibrant, supportive environment.
- Qualifications: Degree in computer science or related field; experience in performance modelling and optimisation.
- Other info: Dynamic role with excellent career growth and a prime location near Cambridge station.
The predicted salary is between 36000 - 60000 £ per year.
CommonAI CIC is a non-profit membership organisation, founded on a belief in collaborative engineering for the safe and responsible development of foundational AI technologies. A place where AI startups, enterprises large and small, public sector bodies and academia can share resources and knowledge, to co-develop and grow businesses, fast.
We are seeking a Performance Engineer to join our rapidly growing team. In this role, you will work with AI researchers and software engineers to build up a detailed understanding of how their applications are performing. You will instrument and collect granular metrics from inference and training jobs and use that information to develop sophisticated mathematical models that predict how software optimisations and architectural or hardware changes will impact system performance. Your work will directly influence both our in-house and member’s hardware purchasing decisions and architectural optimisations, ensuring teams can run AI workloads efficiently and cost-effectively.
This role requires a degree in computer science, mathematics or an adjacent field. You should also be able to demonstrate:
- Experience building insightful mathematical models and performance calculators (Excel/Google Sheets or Python modeling experience) to forecast system behaviour.
- Optimisation of code running on GPUs and/or other accelerators (e.g. CUDA).
- Solid understanding of computer architecture fundamentals and how LLMs and Deep Learning models execute on that hardware (inference vs. training, matrix multiplication, KV-caching, etc.).
- Proficiency with profiling tools (NVIDIA Nsight, PyTorch Profiler) and monitoring stacks (Prometheus, Grafana).
- Capability to work in Python for data analysis (Pandas, NumPy) and scripting.
The following are also highly valued:
- Post-graduate degrees and research experience in relevant fields (please list your publications).
- Deep understanding of inference serving frameworks (e.g. vLLM).
- Background in statistical analysis.
- Contributions to open source and/or research projects.
A collaborative and supportive work environment.
The opportunity to have a high impact in a growing organisation.
Competitive salary package and pension.
Professional development opportunities.
Networking opportunities with influential people from across the tech sector and academia.
A vibrant office environment located a few minutes’ walk away from Cambridge train station.
HPC Engineer employer: CommonAI CIC
Contact Detail:
CommonAI CIC Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land HPC Engineer
✨Tip Number 1
Network like a pro! Reach out to people in the AI and HPC communities, attend meetups, and engage on platforms like LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your mathematical models, performance calculators, and any projects you've worked on. This will give potential employers a clear view of what you can bring to the table.
✨Tip Number 3
Prepare for interviews by brushing up on your knowledge of computer architecture and profiling tools. Be ready to discuss how you've optimised code on GPUs and how you approach performance analysis.
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you're genuinely interested in joining our collaborative team at CommonAI.
We think you need these skills to ace HPC Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the HPC Engineer role. Highlight your experience with mathematical models, performance optimisation, and any relevant tools you've used. We want to see how your skills align with what we're looking for!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about AI and how your background makes you a great fit for our team. Don’t forget to mention any collaborative projects or experiences that resonate with our mission.
Showcase Your Projects: If you've worked on any relevant projects, whether in school or professionally, make sure to include them. We love seeing practical applications of your skills, especially if they involve performance engineering or AI workloads!
Apply Through Our Website: We encourage you to apply through our website for a smoother application process. It helps us keep everything organised and ensures your application gets the attention it deserves. Plus, it’s super easy!
How to prepare for a job interview at CommonAI CIC
✨Know Your Maths and Models
Brush up on your mathematical modelling skills before the interview. Be ready to discuss how you've built performance calculators in Excel or Python, and be prepared to share specific examples of how these models have influenced system performance in your past roles.
✨Show Off Your Optimisation Skills
Make sure you can talk about your experience with code optimisation, especially on GPUs. If you've worked with CUDA or similar technologies, highlight that experience and be ready to explain how you approached optimising code for better performance.
✨Familiarise Yourself with Profiling Tools
Get comfortable with profiling tools like NVIDIA Nsight and PyTorch Profiler. During the interview, you might be asked to explain how you've used these tools to gather metrics and improve application performance, so having concrete examples will help you stand out.
✨Understand the Bigger Picture
Demonstrate your understanding of computer architecture fundamentals and how AI models execute on hardware. Be prepared to discuss concepts like inference vs. training and matrix multiplication, as this knowledge is crucial for the role and shows your depth of understanding.