Systems Research Engineer - LLM Optimisation; vLLM​/TensorRT-LLM

Systems Research Engineer - LLM Optimisation (vLLM / Tensor

RT-LLM)

Permanent

Edinburgh City Centre (On-site 5 days), walking distance from local transport links

Salary :
Competitive and negotiable, 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, Machine learning infrastructure. You will work closely with architects to prototype and optimize the next generation of global AI clusters.

• 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, Euro Sys, 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.

• 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 / Tensor
  
  RT-LLM / PyTorch)
• Hands-on experience with distributed KV cache optimization
• Familiarity with GPU 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