What are the responsibilities and job description for the AI Compiler & GPU Performance Engineer position at Advanced Micro Devices, Inc?
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Job Title: AI Compiler & GPU Performance Engineer
About Us:
Wave is a programming language that aims to redefine the way we program AI kernels and workloads on AMD GPUs. Adopting a unique subgroup programming paradigm with strong support for reasoning about kernel behavior using symbolic expressions, Wave makes it easy to get high performance and do it fast. Join us as we forge the future of machine learning acceleration.
Job Summary:
We are seeking a highly motivated and experienced compiler and GPU Performance Engineer to play a foundational architectural role in the development of Wave. The ideal candidate will possess a deep understanding of compiler design, optimization techniques, and GPU hardware, coupled with a passion for pushing the boundaries of high-performance computing in the field of artificial intelligence.
Responsibilities:
Design and implement advanced compiler optimization passes
Develop and optimize GPU kernels
Define and contribute to the design of language features
Integrate Wave seamlessly with the PyTorch ecosystem
Develop and maintain performance analysis tools and methodologies to identify and address performance bottlenecks.
Stay abreast of the latest advancements in compiler technology, GPU architectures, and machine learning hardware.
Qualifications:
Required:
A strong passion for compiler optimization and GPU performance within the domain of machine learning. A comprehensive understanding of compiler design and its interaction with GPU hardware. Demonstrated proficiency in parallel computing languages such as HIP, CUDA, OpenCL, or Vulkan. Practical experience with GPU architectures from leading vendors such as NVIDIA and AMD. Experience with performance analysis and profiling tools. Excellent problem-solving and analytical skills. Strong communication and collaboration abilities.
Preferred:
A documented history of significant contributions to production-quality compiler projects.
A robust understanding of advanced compiler optimization techniques, particularly those relevant to parallel computing.
Familiarity with compiler intermediate representations, with experience in MLIR being highly advantageous.
A proven track record in the development and optimization of high-performance GPU applications.
Experience with leading machine learning frameworks such as PyTorch and TensorFlow. Familiarity with low-level performance tuning and the impact of hardware architecture on software performance.
Education:
A PhD or Master's degree in Computer Science, Engineering, or a closely related discipline with a significant focus on compiler design, parallel computing, or high-performance computing is typically expected. Candidates with exceptional industry experience in lieu of an advanced degree will also be considered.
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