苹果Technology Evangelist - AI
社招全职Marketing地点:上海状态:招聘♡ 收藏
工作描述
Qualifications Minimum Qualifications 8+ years of software development experience, with a multi-year track record of hands-on AI/ML engineering well beyond integrating pre-built or commercial APIs. Solid grasp of modern AI architectures (LLMs, Diffusion, Multimodal models) across the full lifecycle: training, conversion, quantization, and edge deployment. Battle-tested expertise in AI inference optimization, custom runtimes, serving engines, and pipeline acceleration (proficiency in MLX, Core ML / Core AI, ONNX Runtime, TensorRT, vLLM, or equivalent). Exceptional hands-on coding and debugging skills, with the ability to dive into complex codebases and resolve deep architectural roadblocks. Effective and influential communicator with the ability to quickly understand others' needs, make strategic decisions, and lead execution. Fluency in English and Mandarin is required to effectively engage partners at all levels. Preferred Qualifications Degree in a STEM discipline (e.g., computer science, information technology, math, or physics), or equ…
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包括英文材料
MLX+
[英文] MLX documentation
https://ml-explore.github.io/mlx/build/html/index.html
MLX is a NumPy-like array framework designed for efficient and flexible machine learning on Apple silicon, brought to you by Apple machine learning research.
[英文] MLX
https://mlx-framework.org/
MLX is an array framework for efficient and flexible machine learning on Apple silicon.
Core ML+
[英文] Getting Started
https://apple.github.io/coremltools/docs-guides/source/introductory-quickstart.html
Core ML Tools can convert trained models from other frameworks into an in-memory representation of the Core ML model.
https://developer.apple.com/machine-learning/core-ml/
Core ML is optimized for on-device performance of a broad variety of model types by leveraging Apple silicon and minimizing memory footprint and power consumption.
https://www.youtube.com/watch?v=g3yj9_DHrME
Bring the power of machine learning directly to your apps with Core ML.
ONNX+
https://github.com/onnx/tutorials
Open Neural Network Exchange (ONNX) is an open standard format for representing machine learning models.
[英文] Introduction to ONNX
https://onnx.ai/onnx/intro/
This documentation describes the ONNX concepts (Open Neural Network Exchange).
TensorRT+
https://docs.nvidia.com/deeplearning/tensorrt/latest/getting-started/quick-start-guide.html
This TensorRT Quick Start Guide is a starting point for developers who want to try out the TensorRT SDK; specifically, it demonstrates how to quickly construct an application to run inference on a TensorRT engine.
vLLM+
https://www.newline.co/@zaoyang/ultimate-guide-to-vllm--aad8b65d
vLLM is a framework designed to make large language models faster, more efficient, and better suited for production environments.
https://www.youtube.com/watch?v=Ju2FrqIrdx0
vLLM is a cutting-edge serving engine designed for large language models (LLMs), offering unparalleled performance and efficiency for AI-driven applications.
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