At a Glance
- Tasks: Architect and optimise cutting-edge AI systems for large-scale data centres.
- Company: Leading AI infrastructure company based in Edinburgh.
- Benefits: Competitive salary, generous benefits, and opportunities for research publications.
- Other info: Collaborative environment with excellent career growth and research opportunities.
- Why this job: Join a pioneering team reshaping AI technology and make a real-world impact.
- Qualifications: Degree in Computer Science or related field; experience with LLM optimisation preferred.
The predicted salary is between 60000 - 80000 € per year.
Permanent position in Edinburgh City Centre (On-site 5 days), within walking distance from local transport links.
Salary: Competitive and negotiable, with a generous benefits package.
In an era where Large Language Models (LLMs) are rebuilding the foundational software stack, our client is at the forefront of reshaping how large-scale models are trained, served, and deployed. Operating at the intersection of advanced systems research and industrial-scale engineering, their Edinburgh-based team is driving new AI Infrastructure & Agentic Serving architectures.
This role is a unique opportunity to help define next-generation large-scale data centres and AI infrastructure systems, turning innovative system designs into deployable, real-world technologies.
We are seeking Systems Research Engineers with a deep passion for computer systems, distributed AI infrastructure, and performance optimization. These roles are ideal for recent PhD graduates or exceptional BSc/MSc engineers looking to build research-driven experience in Operating Systems, Distributed Systems, AI Model Serving, and Machine Learning infrastructure. You will work closely with architects to prototype and optimize the next generation of global AI clusters.
What you will be doing:
- Distributed Systems Research & Development: Architect, implement, and evaluate distributed system components for emerging AI and data-centric workloads. Drive modular design and scalability across GPU and NPU clusters, building highly efficient serving and scheduling systems.
- Performance Optimization & Profiling: Conduct in-depth profiling and performance tuning of large-scale inference and data pipelines, focusing on KV cache management, heterogeneous memory scheduling, and high-throughput inference serving using frameworks like vLLM, Ray Serve, and modern PyTorch Distributed systems.
- Scalable Model Serving Infrastructure: Develop and evaluate frameworks that enable efficient multi-tenant, low-latency, and fault-tolerant AI serving across distributed environments. Research and prototype new techniques for cache sharing, data locality, and resource orchestration and scheduling within AI clusters.
- Research & Publications: Translate innovative research ideas into publishable contributions at leading venues (e.g., OSDI, NSDI, EuroSys, SoCC, MLSys, NeurIPS, ICML, ICLR) while driving internal adoption of novel methods and architectures.
- Cross-Team Collaboration: Communicate technical insights, research progress, and evaluation outcomes effectively to multidisciplinary stakeholders and global research teams.
What we are looking for:
- Bachelor's or Master's degree in Computer Science, Electrical Engineering, or related field.
- Fresh PhD graduates in systems, distributed computing, or large-scale AI infrastructure are also welcome.
- At least 2 years of experience with LLM inference/serving framework optimization (vLLM/Ray Serve/TensorRT-LLM/PyTorch).
- Hands-on experience with distributed KV cache optimization.
- Familiarity with GPUs and how they execute LLMs.
- Strong knowledge of distributed systems, operating systems, machine learning systems architecture, inference serving, and AI infrastructure.
- Solid grounding in systems research methodology, distributed algorithms, and profiling tools.
- Proficiency in C/C++, with additional experience in Python for research prototyping.
- Team-oriented mindset with effective technical communication skills.
If this sounds like a role you can take hold of, we would love to hear from you! To apply for this role, please send your CV to Maggie Kwong at maggie.kwong@projectpeople.com.
Great journeys start here, apply now!
AI Systems Research Engineer - LLM Optimisation in Edinburgh employer: LinkedIn
Join a pioneering team in Edinburgh that is redefining AI infrastructure and large-scale model deployment. With a strong emphasis on innovation, collaboration, and professional growth, this company offers a competitive salary and a generous benefits package, alongside opportunities for research-driven experience and contributions to leading publications. The vibrant work culture encourages creativity and teamwork, making it an ideal environment for passionate engineers eager to make a meaningful impact in the field of AI.
StudySmarter Expert Advice🤫
We think this is how you could land AI Systems Research Engineer - LLM Optimisation in Edinburgh
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at local meetups. 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 or GitHub repository showcasing your projects related to distributed systems and AI infrastructure. This gives potential employers a taste of what you can do beyond your CV.
✨Tip Number 3
Prepare for interviews by brushing up on technical concepts and common questions in your field. Practice explaining your past projects and how they relate to the role you're applying for, especially around LLM optimisation.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love hearing from passionate candidates who are eager to join our team.
We think you need these skills to ace AI Systems Research Engineer - LLM Optimisation in Edinburgh
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that align with the AI Systems Research Engineer role. Highlight your experience with distributed systems, LLM optimisation, and any relevant projects you've worked on.
Craft a Compelling Cover Letter:Use your cover letter to tell us why you're passionate about AI infrastructure and how your background makes you a great fit for our team. Be sure to mention specific technologies or methodologies you've worked with that relate to the job.
Showcase Your Research Experience:If you've published any research or have experience in systems research methodology, make it stand out! We love seeing candidates who can translate innovative ideas into practical applications, so don’t hold back.
Apply Through Our Website:We encourage you to apply directly through our website for a smoother application process. It helps us keep track of your application and ensures you’re considered for this exciting opportunity!
How to prepare for a job interview at LinkedIn
✨Know Your Stuff
Make sure you brush up on your knowledge of distributed systems and AI infrastructure. Be ready to discuss specific frameworks like vLLM and Ray Serve, as well as your experience with performance optimisation. The more you can demonstrate your expertise, the better!
✨Showcase Your Projects
Prepare to talk about any relevant projects you've worked on, especially those involving LLM inference or distributed computing. Highlight your role in these projects and the impact they had. This will show your practical experience and problem-solving skills.
✨Ask Smart Questions
Come prepared with insightful questions about the company's current projects or future directions in AI infrastructure. This not only shows your interest but also your understanding of the field. It’s a great way to engage with the interviewers and make a lasting impression.
✨Communicate Clearly
Since this role involves cross-team collaboration, practice explaining complex technical concepts in simple terms. Being able to communicate effectively with multidisciplinary teams is crucial, so showcase your ability to break down intricate ideas during the interview.
