At a Glance
- Tasks: Join a cutting-edge team to tackle modern AI challenges and build scalable systems.
- Company: One of the largest telecommunications companies globally, based in Edinburgh.
- Benefits: Competitive salary, collaborative environment, and opportunities for impactful research.
- Other info: Work in a vibrant tech ecosystem with excellent career growth potential.
- Why this job: Make a real impact on the future of AI infrastructure and technology.
- Qualifications: Bachelor’s or Master’s in CS/EE, strong C/C++ skills, and experience with LLM frameworks.
The predicted salary is between 36000 - 60000 £ per year.
One of the largest telecommunications companies in the world is looking for an experienced researcher to join the company in Edinburgh.
The Vision
We are currently scaling a world-class research team in Edinburgh to redefine the foundational software stack for the LLM era. As AI transitions from experimental to "agentic" and "AI-native" infrastructure, we are building the super-node clusters and distributed architectures that will power the next generation of global data centres.
This is a unique hybrid role positioned at the intersection of academic-grade systems research and industrial-scale engineering. You won’t just be writing papers; you’ll be prototyping and deploying the frameworks that manage GPU/NPU clusters at a massive scale.
The Technical Challenge
- Distributed Systems R&D: Architecting components for CPU, GPU, and NPU clusters with a focus on modularity and extreme scalability.
- Performance Engineering: In-depth profiling of large-scale inference pipelines, specifically focusing on KV cache management and heterogeneous memory scheduling.
- AI Serving: Optimising high-throughput frameworks (vLLM, Ray Serve, PyTorch Distributed) to ensure low-latency, multi-tenant performance.
- Research Leadership: Contributing to top-tier venues (OSDI, NSDI, EuroSys, MLSys) and driving those innovations into real-world production.
Who You Are
We are looking for "systems-first" thinkers—engineers who understand what happens under the hood of a cluster.
Required Experience:
- Education: A Bachelor’s or Master’s in CS, EE, or a related field (PhD highly preferred).
- The Stack: Strong proficiency in C/C++ for systems work, with Python for rapid prototyping.
- Expertise: Hands-on experience with LLM serving frameworks (vLLM, Ray Serve, TensorRT-LLM) and distributed algorithms.
- Mindset: A solid grounding in systems research methodology and performance profiling tools.
The "Value Add" (Desired):
- A PhD focused on distributed computing or AI infrastructure.
- A track record of publications at major conferences (NeurIPS, ICML, ICLR, etc.).
- Deep knowledge of load balancing, fault tolerance, and resource orchestration in massive AI clusters.
Why Join This Team?
- Impact: Work on one of the largest R&D footprints globally.
- Collaboration: Partner with senior architects and global research teams to solve problems that don't have "off-the-shelf" solutions yet.
- Location: Based in the heart of Edinburgh’s thriving tech ecosystem.
Interested? Apply directly through LinkedIn, or send your CV to george@eu-recruit.com.
Systems Research Engineer in Edinburgh employer: European Tech Recruit
Join a leading global technology organisation in Edinburgh, where innovation meets collaboration. As an AI Engineer, you'll be part of a dynamic research and development team dedicated to pushing the boundaries of multimodal AI and edge computing. Enjoy a supportive work culture that fosters professional growth, offers competitive benefits, and provides unique opportunities to work on groundbreaking projects that shape the future of intelligent mapping and spatial reasoning systems.
StudySmarter Expert Advice🤫
We think this is how you could land Systems Research Engineer in Edinburgh
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at local meetups. A friendly chat can open doors that a CV just can't.
✨Tip Number 2
Show off your skills! If you’ve got projects or prototypes, share them online. A GitHub repo or a personal website can really make you stand out.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Practice common questions and scenarios related to systems engineering.
✨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, we love seeing candidates who take that extra step.
We think you need these skills to ace Systems Research Engineer in Edinburgh
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights your experience with C/C++ and any relevant systems research. We want to see how your skills align with the role, so don’t be shy about showcasing your projects and achievements!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you’re passionate about systems engineering and how your background makes you a perfect fit for our team. Keep it engaging and personal!
Showcase Your Research Experience:If you've got publications or projects related to distributed systems or AI infrastructure, make sure to mention them. We love seeing candidates who have a strong research background and can bring that knowledge into practical applications.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re serious about joining our team!
How to prepare for a job interview at European Tech Recruit
✨Know Your Tech Stack
Make sure you’re well-versed in C/C++ and Python, as these are crucial for the role. Brush up on your knowledge of LLM serving frameworks like vLLM and Ray Serve, and be ready to discuss how you've used them in past projects.
✨Showcase Your Research Experience
Prepare to talk about any publications or research projects you've been involved in, especially those related to distributed computing or AI infrastructure. Highlight your contributions and how they can translate into real-world applications.
✨Understand the Challenges
Familiarise yourself with the bottleneck problems in modern AI, such as KV cache management and heterogeneous memory scheduling. Be ready to discuss potential solutions and your approach to performance engineering.
✨Demonstrate a Systems-First Mindset
During the interview, convey your understanding of what happens under the hood of a cluster. Discuss your experience with load balancing, fault tolerance, and resource orchestration, and how these concepts apply to large-scale systems.
