AMD大模型算法实习生（模型量化/低比特训练推理）Jan - Jun 2026
实习兼职地点：北京状态：招聘
任职要求
职位描述 研究和开发大模型优化算法，包括但不限于模型（如CNN、LLM、Vision LLM等）的量化、剪枝、蒸馏、压缩等算法设计与实现，提高训练和推理性能 分析模型和数据类型特点，制定对应的推理优化与量化策略，鼓励创新和论文发表； 关注低比特数据类型的模型训练，分析和解决精度与性能问题 跟踪AI领域最新研究成果和技术动态（特别是大模型量化和推理加速技术），评估其在实际产品中的应用潜力和落地价值，提出改进和创新的想法，推动团队的技术发展。 职位要求 扎实的C/C++或者Python基础，熟悉至少一种深度学习框架（如PyTorch、TensorFlow、JAX等）； 对AI算法和应用有深刻的理解，熟练掌握机器学习、深度学习、强化学习等理论知识，具备模型训练与部署的完整开发经验；或者有模型量化/剪枝/蒸馏/压缩相关科研或项目经验；或了解Flash Attenti…
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工作职责
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包括英文材料
大模型+
https://www.youtube.com/watch?v=xZDB1naRUlk
You will build projects with LLMs that will enable you to create dynamic interfaces, interact with vast amounts of text data, and even empower LLMs with the capability to browse the internet for research papers.
https://www.youtube.com/watch?v=zjkBMFhNj_g
算法+
https://roadmap.sh/datastructures-and-algorithms
Step by step guide to learn Data Structures and Algorithms in 2025
https://www.hellointerview.com/learn/code
A visual guide to the most important patterns and approaches for the coding interview.
https://www.w3schools.com/dsa/
CNN+
https://learnopencv.com/understanding-convolutional-neural-networks-cnn/
Convolutional Neural Network (CNN) forms the basis of computer vision and image processing.
[英文] CNN Explainer
https://poloclub.github.io/cnn-explainer/
Learn Convolutional Neural Network (CNN) in your browser!
https://www.deeplearningbook.org/contents/convnets.html
Convolutional networks(LeCun, 1989), also known as convolutional neuralnetworks, or CNNs, are a specialized kind of neural network for processing data.
https://www.youtube.com/watch?v=2xqkSUhmmXU
MIT Introduction to Deep Learning 6.S191: Lecture 3 Convolutional Neural Networks for Computer Vision
C+
https://www.freecodecamp.org/chinese/news/the-c-beginners-handbook/
本手册遵循二八定律。你将在 20% 的时间内学习 80% 的 C 编程语言。
https://www.youtube.com/watch?v=87SH2Cn0s9A
https://www.youtube.com/watch?v=KJgsSFOSQv0
This course will give you a full introduction into all of the core concepts in the C programming language.
https://www.youtube.com/watch?v=PaPN51Mm5qQ
In this complete C programming course, Dr. Charles Severance (aka Dr. Chuck) will help you understand computer architecture and low-level programming with the help of the classic C Programming language book written by Brian Kernighan and Dennis Ritchie.
C+++
https://www.learncpp.com/
LearnCpp.com is a free website devoted to teaching you how to program in modern C++.
https://www.youtube.com/watch?v=ZzaPdXTrSb8
Python+
https://liaoxuefeng.com/books/python/introduction/index.html
中文，免费，零起点，完整示例，基于最新的Python 3版本。
https://www.learnpython.org/
a free interactive Python tutorial for people who want to learn Python, fast.
https://www.youtube.com/watch?v=K5KVEU3aaeQ
Master Python from scratch 🚀 No fluff—just clear, practical coding skills to kickstart your journey!
https://www.youtube.com/watch?v=rfscVS0vtbw
This course will give you a full introduction into all of the core concepts in python.
深度学习+
https://d2l.ai/
Interactive deep learning book with code, math, and discussions.
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.
TensorFlow+
https://www.youtube.com/watch?v=tpCFfeUEGs8
Ready to learn the fundamentals of TensorFlow and deep learning with Python? Well, you’ve come to the right place.
https://www.youtube.com/watch?v=ZUKz4125WNI
This part continues right where part one left off so get that Google Colab window open and get ready to write plenty more TensorFlow code.
JAX+
https://docs.jax.dev/en/latest/notebooks/thinking_in_jax.html
JAX is a library for array-oriented numerical computation, with automatic differentiation and JIT compilation to enable high-performance machine learning research.
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实习算法序列
职位概览 我们正在寻找对端到端自动驾驶、多模态大模型（VLM/VLA）以及模型高效部署充满热情的同学。你将参与开发下一代智能驾驶算法，重点解决大参数量模型在车规级芯片上的实时运行难题，通过量化、剪枝及训练策略优化，让 AI 更好地理解物理世界并执行驾驶决策。 核心职责 大模型研发与优化：参与视觉语言模型（VLM）或视觉-语言-动作模型（VLA）在自动驾驶场景下的预训练、微调（SFT）及指令遵循能力优化。 量化算法实施：针对 Transformer/Diffusion 等架构，研究并落地先进的量化算法（如 PTQ、QAT、FP8/INT8/INT4 量化），确保模型在有限算力下保持精度。 算法端到端部署：配合工程团队，将复杂的感知或决策模型转化为高效的推理引擎，解决量化掉点、算子融合等实际问题。 前沿技术跟踪：调研并复现相关的顶会论文（CVPR, ICCV, NeurIPS 等），探索大模型在自动驾驶长尾场景（Corner Cases）中的应用。
更新于 2026-04-02南京