3 of 3 Permanent PyTorch Jobs in Edinburgh

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 ...

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 … 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 GPU and how they execute LLMs Strong knowledge of distributed systems, operating systems, machine learning systems ...

performance tuning of inference pipelines, focusing on KV cache management. Develop low-latency, fault-tolerant AI serving frameworks using vLLM, Ray Serve, and PyTorch Distributed. Research and prototype novel techniques for cache sharing, data locality, and resource orchestration. Translate innovative designs into publishable contributions at top-tier venues (e.g., OSDI … Infrastructure/LLM Serving/vLLM/KV Cache/Performance Optimization/Machine Learning Systems/C++/Python/Ray Serve/PyTorch Distributed/Cloud Infrastructure If you are interested in this Systems Research Engineer position, please send a copy of your CV to ts@eu-recruit.com ...