英伟达Principal On-Device Model Inference Optimization Engineer
社招全职地点:北京 | 上海状态:停招♡ 收藏
工作描述
任职要求 • MSc or PhD in Computer Science, Engineering, or a related field, or equivalent experience. • Over 10 years of confirmed experience specializing in model inference and optimization. • 15+ overall years of work experience in a relevant area • Expertise in modern machine learning frameworks, particularly PyTorch, ONNX, and TensorRT. • Proven experience in optimizing inference for transformer and convolutional architectures. • Strong programming proficiency in CUDA, Python, and C++. • In-depth knowledge of optimization techniques, including quantization, pruning, distillation, and hardware-aware neural architecture search. • Skilled in building and deploying scalable, cloud-based inference systems. • Passionate about developing efficient, production-ready solutions with a strong focus …
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包括英文材料
PyTorch+
https://datawhalechina.github.io/thorough-pytorch/
PyTorch是利用深度学习进行数据科学研究的重要工具,在灵活性、可读性和性能上都具备相当的优势,近年来已成为学术界实现深度学习算法最常用的框架。
https://www.youtube.com/watch?v=V_xro1bcAuA
Learn PyTorch for deep learning in this comprehensive course for beginners. PyTorch is a machine learning framework written in Python.
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.
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