AI Systems Research Engineer - LLM Optimisation

<p><strong>AI Systems Research Engineer - LLM Optimisation</strong></p><p><strong>Permanent</strong></p><p><strong>Edinburgh City Centre (On-site 5 days), walking distance from local transport links </strong></p><p><strong>Salary : Competitive and negotiable, generous benefits package </strong></p><p><br><br>You could be just the right applicant for this job Read all associated information and make sure to apply.<br></p><p><br></p><p>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.</p><p><br></p><p>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.</p><p><br></p><p>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.</p><p><br></p><p><br></p><p><strong><u>What you will be doing</u></strong></p><p><br></p><ul><li><strong>Distributed Systems Research & Development : </strong>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.</li><li><strong>Performance Optimization & Profiling : </strong>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.</li><li><strong>Scalable Model Serving Infrastructure : </strong>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.</li><li><strong>Research & Publications : </strong>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.</li><li><strong>Cross-Team Collaboration : </strong>Communicate technical insights, research progress, and evaluation outcomes effectively to multidisciplinary stakeholders and global research teams.</li></ul><p><br></p><p><br></p><p><strong><u>What we are looking for</u></strong></p><p><br></p><ul><li>Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or related field</li><li>Fresh PhD graduates in systems, distributed computing, or large-scale AI infrastructure are also welcome</li><li>At least 2 years of experience with LLM inference / serving framework optimization (vLLM / Ray Serve / TensorRT-LLM / PyTorch)</li><li>Hands-on experience with distributed KV cache optimization</li><li>Familiarity with GPU and how they execute LLMs</li><li>Strong knowledge of distributed systems, operating systems, machine learning systems architecture, Inference serving, and AI Infrastructure.</li><li>Solid grounding in systems research methodology, distributed algorithms, and profiling tools. xwzovoh </li><li>Proficiency in C/C++, with additional experience in Python for research prototyping.</li><li>Team-oriented mindset with effective technical communication skills</li></ul><p><br></p><p>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</p><p> </p><p><br></p><p>Great journeys start here, apply now!</p>