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: Enjoy a competitive salary, pension, and professional development in a vibrant office.
- Other info: Network with industry leaders and contribute to innovative AI projects.
- Why this job: Make a real impact in the AI field while working with cutting-edge technologies.
- Qualifications: Degree in computer science or related field; experience with performance modelling and optimisation.
The predicted salary is between 50000 - 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.
Requirements
- Degree in computer science, mathematics or an adjacent field.
- Experience building insightful mathematical models and performance calculators (Excel/Google Sheets or Python modelling 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.
Highly Valued Qualifications
- 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.
Benefits
- 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.
Performance Engineer in Cambridge employer: CommonAI CIC
Contact Detail:
CommonAI CIC Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Performance Engineer in Cambridge
✨Tip Number 1
Network like a pro! Reach out to people in the AI and performance engineering space. Attend meetups, webinars, or even just grab a coffee with someone in the field. You never know who might have a lead on your dream job!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your mathematical models and performance calculators. Use GitHub to share your projects and contributions to open source. This will make you stand out when applying through our website.
✨Tip Number 3
Prepare for interviews by brushing up on your knowledge of computer architecture and profiling tools. Practice explaining complex concepts in simple terms. This will help you connect with interviewers and demonstrate your expertise.
✨Tip Number 4
Don’t hesitate to follow up after interviews! A quick thank-you email can go a long way. It shows your enthusiasm for the role and keeps you fresh in their minds as they make decisions.
We think you need these skills to ace Performance Engineer in Cambridge
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Performance 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 Technical Skills: Be sure to include specific examples of your technical skills in your application. Whether it's your proficiency in Python, experience with profiling tools, or understanding of computer architecture, we want to know how you can contribute to our projects!
Apply Through Our Website: We encourage you to apply through our website for a smoother process. It helps us keep track of applications and ensures you get all the updates directly from us. Plus, it shows you're keen on joining our community!
How to prepare for a job interview at CommonAI CIC
✨Know Your Metrics
Before the interview, brush up on the key metrics and performance indicators relevant to AI applications. Be ready to discuss how you would instrument and collect granular metrics from inference and training jobs, as this will show your understanding of the role's requirements.
✨Showcase Your Modelling Skills
Prepare examples of mathematical models or performance calculators you've built in the past. Whether it's using Excel, Google Sheets, or Python, being able to demonstrate your experience with forecasting system behaviour will set you apart from other candidates.
✨Understand the Hardware
Familiarise yourself with computer architecture fundamentals, especially how LLMs and Deep Learning models execute on hardware. Be prepared to discuss specific optimisations for GPUs and accelerators like CUDA, as this knowledge is crucial for the Performance Engineer role.
✨Get Hands-On with Tools
Make sure you're comfortable with profiling tools like NVIDIA Nsight and PyTorch Profiler, as well as monitoring stacks such as Prometheus and Grafana. Being able to talk about your experience with these tools will demonstrate your technical proficiency and readiness for the job.