AI Compiler Optimization Engineer - Edinburgh

microTECH Global Limited; City of Edinburgh; Hybrid; GBP 90,000 - 120,000; Full time

Job summary

microTECH Global Limited in the United Kingdom is seeking an AI Compiler Optimization Engineer to push inference performance on CPU and hybrid CPU/XPU systems. You will implement advanced compiler techniques and profiling to uncover optimization opportunities.

You will profile frameworks such as TensorFlow, PyTorch, ONNX, and llama.cpp, propose kernel and graph-level optimizations, and contribute insights from industry research through technical reports.

Qualifications

• Experience with compiler optimizations for AI workloads.
• Familiar with MLIR/LLVM and JIT graph optimizations.
• Experience profiling AI frameworks and identifying bottlenecks.

Responsibilities

• Implement compiler techniques to enhance inference performance on CPU and CPU/XPU hybrid systems.
• Profile and optimize end-to-end inference workflows across AI frameworks.
• Track AI/compiler research and publish actionable insights.

Skills

Compiler optimizations

MLIR development

LLVM backend

Performance profiling

JIT optimization

Tools

LLVM

MLIR

TensorFlow

PyTorch

ONNX

llama.cpp

Job description

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