AI Compiler Optimization Engineer - Edinburgh

Job Description

We are seeking a skilled AI Compiler Optimization Engineer to optimize AI model inference performance through advanced compiler technologies.

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You will focus on performance tuning for CPU or hybrid CPU/XPU heterogeneous architectures, profiling AI frameworks to discover new optimization opportunities, and delivering cutting-edge insights from industry research.Key Responsibilities:Compiler-Based Performance Optimization:Implement compiler techniques (e.g., MLIR level optimizations, LLVM backend optimizations) to enhance inference performance on CPU and CPU/XPU hybrid systemsOptimize JIT level compute graphs with operator fusion, memory allocation and etc.

for latency/throughput improvementsPreferred: Experience with LLVM/MLIR developmentAI Model Profiling & Framework Optimization:Profile end-to-end inference workflows on frameworks like TensorFlow, PyTorch, ONNX, and llama.cpp to identify hotspots and bottlenecksPropose and implement optimization strategies (e.g., kernel tuning, graph-level optimizations)Preferred: Experience optimizing xwzovoh models on multiple AI frameworksResearch & Insight Development:Track and analyze the latest advancements in AI & compiler research (academic papers, open-source projects)Produce actionable insight reports summarizing trends, benchmarks, and potential optimizationsPreferred: Strong technical writing skills with prior publications or reports