Systems Research Engineer

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 By applying to this role you understand that we may collect your personal data and store and process it on our systems. For more information please see our Privacy Notice