Adreno GPU AI Compiler Perf specialist

Conduct competitive analysis of GPU compiler and performance characteristics across industry leaders (AMD, Intel, Nvidia, ARM), identifying strengths and areas for improvement. Profile and characterize trending GPU benchmarks and applications (games, HPC, and AI applications), comparing results with competitor platforms. Utilize external and internal profiling tools, including AI-powered analytics, to analyze performance data and identify bottlenecks. Apply AI and machine learning methodologies to optimize compiler algorithms and improve GPU workload performance. Integrate and evaluate AI-based graphics techniques for enhanced rendering, upscaling, denoising, and other visual improvements. Propose improvements in compilers and GPU architecture, informed by competitive analysis and AI-driven insights. Summarize profiling results and present findings to customers and internal teams, including comparative performance reports. Collaborate on the development of new AI-based performance modeling and benchmarking tools. Support performance analysis and optimization for the Snapdragon Elite Windows on Arm platform. Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 4+ years of Systems Engineering or related work experience. OR Master's degree in Engineering, Information Systems, Computer Science, or related field and 3+ years of Systems Engineering or related work experience. OR PhD in Engineering, Information Systems, Computer Science, or related field and 2+ years of Systems Engineering or related work experience. BS/MS/PhD degree in Computer Science, Electrical Engineering, or Game Development. Experience in compiler development, with exposure to AI-based optimization techniques. Hands-on experience with LLVM compiler infrastructure, including developing, optimizing, and debugging LLVM-based GPU compilers. Familiarity with LLVM IR, pass development, and integration of AI-based optimization techniques within the LLVM framework. Understanding of computer architecture (GPU, memory, data layout, etc.) and performance tradeoffs, including comparative analysis with competitor architectures. Proficiency in C/C++ and scripting languages (e.g., Python), with experience in machine learning frameworks. Strong communication skills, teamwork spirit, reliability, and self-motivation. Plus Experience in graphics programming, OpenCL, or CUDA application development. Familiarity with performance profiling tools and hardware performance counters for parallel applications on multicore or manycore architectures. Hands-on experience with machine learning/deep learning tools (scikit-learn, TensorFlow, or similar) for performance analysis and compiler optimization. Experience with benchmarking and performance tuning for parallel applications, including comparative benchmarking against competitor platforms. Experience with performance analysis and optimization for Windows on Arm platforms. Exposure to AI-based graphics techniques such as neural rendering, super-resolution, denoising, and real-time inference for graphics workloads.