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range of industries within the field of technology
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range of industries within the field of technology
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AI Compiler Optimization Engineer
AI Compiler Optimisation Engineer – Contract (inside IR-35)
Role Overview
We are looking for an experienced AI Compiler Optimization Engineer to enhance the efficiency of AI model inference using advanced compiler techniques. This role involves fine-tuning performance on CPU-based and hybrid CPU/XPU systems, analyzing AI frameworks to uncover optimization opportunities, and leveraging the latest research to drive improvements.
Core Responsibilities
Compiler Optimization for Performance Enhancement:
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Apply compiler techniques such as MLIR and LLVM-based optimizations to improve AI inference performance on CPU and CPU/XPU hybrid architectures.
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Enhance JIT compute graph execution by implementing optimizations like operator fusion and efficient memory management to boost throughput and reduce latency.
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Ideal candidates will have prior experience in LLVM/MLIR development.
AI Framework Profiling & Optimization:
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Analyze AI model execution in frameworks such as TensorFlow, PyTorch, ONNX, and llama.cpp to pinpoint inefficiencies and performance bottlenecks.
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Develop and implement optimizations, including kernel-level enhancements and graph-based improvements.
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Preference for candidates with experience optimizing models across multiple AI frameworks.
Research & Technical Insights:
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Stay informed on emerging trends in AI and compiler technologies by reviewing academic publications and open-source advancements.
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Generate detailed reports that translate cutting-edge research into practical performance enhancements.
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Strong technical writing ability, with prior publications or technical reports, is a plus.
Note: This job description provides an outline of primary duties and expectations. Additional responsibilities may be assigned as required by the company’s evolving needs.
Candidate Profile
Essential Qualifications:
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Strong proficiency in C/C++ with experience in compiler infrastructures such as LLVM or MLIR.
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In-depth understanding of AI model structures and inference execution.
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Hands-on experience with profiling tools (e.g., perf, TensorBoard, VTune) for performance analysis.
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Familiarity with CPU/XPU hardware architectures and related optimization techniques.
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Excellent analytical and problem-solving skills.
Preferred Skills & Experience:
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Degree in Computer Science or a related field (BSc/MSc/MSci).
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Contributions to open-source compiler projects (LLVM/MLIR communities preferred).
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Experience working with heterogeneous computing environments (CPU/GPU/XPU).
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Published research or technical content related to AI and compiler optimization.
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Self-motivated, eager to explore new technologies, and skilled in hands-on problem-solving.
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Apply Now
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