4 of 4 Permanent LLVM Jobs in Edinburgh

diversified team with different culture background but share common goals. Your contribution can be up-streamed to open-source communities. Key Responsibilities: Develop LLVM based compilers: Enable compiler for new architecture features Design & implement optimization for xPU architectures Use AI technology to help compiler optimize Analyze performance of compiler ...

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 … 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 ...

Huawei Research and Development UK Limited is seeking a compiler engineer to join a global team based in Cambridge and Edinburgh. You will develop LLVM-based compilers, analyze performance, and optimize for xPU architectures while collaborating with cross-functional teams. The role emphasizes cutting-edge compiler research, open-source ...

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 … 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 ...