Apples Server ML Frameworks team in GPU, Graphics and Machine Learning works on enabling Apple Intelligence through high-performance, distributed inference of GenAI applications (such as LLMs) on Private Cloud Compute. You will get to work on custom-built server hardware that brings the power and security of Apple silicon to the data center. We are looking for engineers with systems background who are deeply passionate about building scalable, efficient, and production-grade solutions tailored for high-throughput GPU execution. Our team is seeking extraordinary machine learning and GPU programming engineers who are passionate about providing robust compute solutions for accelerating Machine learning libraries on Apple Silicon. Role has the opportunity to influence the design of compute and programming models in next generation GPU architectures. Work on cutting-edge ML inference framework project and optimize code for efficient and scalable ML inference using distributed compute strategies such as data, tensor, pipeline and expert parallelism. Develop kernel and compiler level optimizations and perform in-depth analysis to ensure the best possible performance across Server hardware families. Apply advanced model optimization techniques including speculation, quantization, compression, and others to maximize throughput and minimize latency. Collaborate closely with hardware, compiler, and systems teams to align software performance with hardware capabilities. Analyze and improve performance metrics such as end-to-end latency, TTFT, TBOT, memory footprint, and compute efficiency. Requirements: 3+ years of programming and problem-solving experience with C/C++/ObjC Experience with GPU kernel development & optimizations using compute programming models such as Metal, CUDA etc. Experience with Distributed training or inference techniques Experience with system level programming and computer architecture Experience with graph compilers such as CuTE, CuTile, Triton, OpenXLA or LLVM is a plus Good understanding of LLM and Diffusion based model architectures
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