百度昆仑芯-AI芯片软件研发实习生（J83485）
实习兼职ACG地点：北京 | 上海状态：招聘
任职要求
-2026届及以后在校生（微电子，计算机，数学等相关专业） -熟悉Python/C/C++，良好的编程习惯，熟悉计算机基础算法和数据结构，熟悉 Linux 开发环境 -熟悉深度学习相关知识，有框架(Pytorch, PaddlePaddle，TVM，MLIR，ONNX等)开发或使用经验优先 -熟悉CUDA，OpenCL等高性能计算编程经验优先
工作职责
-结合前沿业务场景，构建昆仑芯AI大规模训练推理系统 -负责大模型分布式训练、推理框架的适配与调优，设计千卡级集群通信加速、混合精度训练等方案 -为昆仑芯AI芯片各系列高性能加速芯片提供软件栈，包括框架，图编译器以及周边产品的技术落地 -AI芯片性能深度学习高性能计算库开发，支持各种AI场景，持续提升系统效能
包括英文材料
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.
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
编程规范+
[英文] Google Style Guides
https://google.github.io/styleguide/
Every major open-source project has its own style guide: a set of conventions (sometimes arbitrary) about how to write code for that project. It is much easier to understand a large codebase when all the code in it is in a consistent style.
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/
数据结构+
https://www.youtube.com/watch?v=8hly31xKli0
In this course you will learn about algorithms and data structures, two of the fundamental topics in computer science.
https://www.youtube.com/watch?v=B31LgI4Y4DQ
Learn about data structures in this comprehensive course. We will be implementing these data structures in C or C++.
https://www.youtube.com/watch?v=CBYHwZcbD-s
Data Structures and Algorithms full course tutorial java
Linux+
https://ryanstutorials.net/linuxtutorial/
Ok, so you want to learn how to use the Bash command line interface (terminal) on Unix/Linux.
https://ubuntu.com/tutorials/command-line-for-beginners
The Linux command line is a text interface to your computer.
https://www.youtube.com/watch?v=6WatcfENsOU
In this Linux crash course, you will learn the fundamental skills and tools you need to become a proficient Linux system administrator.
https://www.youtube.com/watch?v=v392lEyM29A
Never fear the command line again, make it fear you.
https://www.youtube.com/watch?v=ZtqBQ68cfJc
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.
PaddlePaddle+
https://learnopencv.com/paddlepaddle/
PaddlePaddle (PArallel Distributed Deep LEarning) is an open-source deep learning framework released by Baidu in 2016.
https://www.paddlepaddle.org.cn/tutorials
本课程采用飞桨特色的「横纵式」 教学法，从易到难，学习难度逐层递进，并结合图形和案例进行讲解，力求让刚接触深度学习的读者可以快速理解。
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).
CUDA+
https://developer.nvidia.com/blog/even-easier-introduction-cuda/
This post is a super simple introduction to CUDA, the popular parallel computing platform and programming model from NVIDIA.
https://www.youtube.com/watch?v=86FAWCzIe_4
Lean how to program with Nvidia CUDA and leverage GPUs for high-performance computing and deep learning.
OpenCL+
https://developer.nvidia.com/opencl
OpenCL™ (Open Computing Language) is a low-level API for heterogeneous computing that runs on CUDA-powered GPUs.
https://engineering.purdue.edu/~smidkiff/ece563/NVidiaGPUTeachingToolkit/Mod20OpenCL/3rd-Edition-AppendixA-intro-to-OpenCL.pdf
we will give a brief overview of OpenCL for CUDA programers.
[英文] Hands On OpenCL
https://handsonopencl.github.io/
An open source two-day lecture course for teaching and learning OpenCL
https://leonardoaraujosantos.gitbook.io/opencl/chapter1
Open Computing Language is a framework for writing programs that execute across heterogeneous platforms.
https://ulhpc-tutorials.readthedocs.io/en/latest/gpu/opencl/
OpenCL came as a standard for heterogeneous programming that enables a code to run in different platforms.
https://www.youtube.com/watch?v=4q9fPOI-x80
This presentation will show how to make use of the GPU from Java using OpenCL.
相关职位
社招3年以上ACG
-负责从芯片、封装到板级、整机的散热设计和仿真分析; 主导昆仑芯AI芯片的散热设计及后续测试、样机制作、优化、转产等；主导输出散热设计方案和仿真报告 -负责芯片级到板级、整机的散热测试计划的制定和实施 -支持产品DVT/PVT等，支持工程、生产、售后等环节相关散热问题的闭环 -负责ODM/OEM厂家散热方案、散热测试计划和测试报告的把关和审查，跟踪散热相关问题的解决 -主导热设计平台的建设，完善热设计评估体系，不断优化热仿真模型精度， 提升热设计能力 -探索热设计、验证、材料工艺方向的新技术，驱动验证、引入、产品化落地
更新于 2025-05-07
社招ACG
-负责昆仑芯AI芯片性能深度学习高性能计算库开发，支持各种AI场景 -探索新一代AI芯片编程模型和架构 -深度学习框架Pytorch/PaddlePaddle图性能优化 -大规模分布式训练性能优化，AI芯片通信库开发，探索高效的芯片互联架构
更新于 2025-04-18
实习ACG
-结合前沿业务场景，构建昆仑AI大规模训练系统 -持续提升系统效能 -主流AI模型对昆仑AI芯片的适配和性能优化工作 -探索昆仑AI芯片分布式训练方案
更新于 2025-05-07
社招ACG
-负责基于昆仑芯AI芯片的自动驾驶高性能计算库的开发及算法优化 -负责基于昆仑芯AI芯片的主流自动驾驶模型训练部署和性能优化工作 -负责Pytorch等深度学习框架的定制和性能优化工作 -负责重点客户的自动驾驶训练业务支持工作 -探索基于昆仑芯AI芯片的自动驾驶端到端大模型技术路径
更新于 2025-05-07