AI Compiler Optimization Engineer

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 Tensor Flow, 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