(Summary generated by AI based on the full job description)
The project focuses on a CoreML team driving performance and optimization of ML training and serving. Key technologies and tools include C++, Python, TPU, GPU, Pallas, Triton, CUDA, JAX and PyTorch. Responsibilities include designing and optimizing high-performance kernels, building benchmarking and autotuning infrastructure, performance analysis, regression testing and collaborating with ML researchers, framework developers and compiler engineers to improve adoption and scalability.

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