AI Compiler Optimization Engineer - Edinburgh

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

Check all associated application documentation thoroughly before clicking on the apply button at the bottom of this description.

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 systems Optimize JIT level compute graphs with operator fusion, memory allocation and etc.

for latency/throughput improvements Preferred: Experience with LLVM/MLIR development AI Model Profiling & Framework Optimization: Profile end-to-end inference workflows on frameworks like TensorFlow, PyTorch, ONNX, and llama.cpp to identify hotspots and bottlenecks Propose and implement optimization strategies (e.g., kernel tuning, graph-level optimizations) Preferred: Experience optimizing models on multiple AI xwzovoh frameworks Research & 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 optimizations Preferred: Strong technical writing skills with prior publications or reports