百度飞桨-机器学习/大模型算法研发工程师-2026AIDU(J85300)
校招全职AIDU项目地点:北京 | 上海 | 深圳状态:招聘
任职要求
精通以下一项或多项专业技能,或在相关领域具备丰富经验; 专业技能: -热爱编程,精通C++/Python; -具有独立开发能力,对AI算法和主流框架有丰富的应用或开发经验; -精通GPU/ARM/MIPS/DSP等任意异构计算平台; -精通计算机体系结构,有汇编级别开发经验; -精通GPU/ARM/MIPS/DSP等异构计算平台的通信库; -精通MPI,对不同网络拓扑结构的通信算法及底层通信函数有深入研究,对RDMA,GPU direct等技术有了解; -对分布式计算有深入理解,特别是同步,异步等通信策略在AI计算中的应用。 软素质: -目标驱动,并有探索精神; -团队协作及沟通能力; -具备分析和解决问题的能力; -有极强的学习能力和知识迁移能力。 具有以下条件者优先: -精通PaddlePaddle、Caffe/Caffe2、MXNET、TensorFlow等开源框架,做过源码级优化移植等工作; -熟练使用Cublas、Cudnn、MIopen、OpenBlas、MKL、Eigen等主流计算库; -熟悉AI training通信过程,熟悉MPI,NCCL,RDMA,GPU Direct等通信技术; -精通CUDA/OpenCL开发,有SASS或PTX级别优化开发经验; -精通Neon或ARM-GPU开发,有过大小端并行计算优化经验及ARM-GPU协同开发经验; -精通常用硬件平台性能分析工具链,如CodeXL\NVVP\GPA等; -精通LLVM; -有Linux内核相关开发和优化经验。
工作职责
我们致力于构建全球领先的AI异构计算加速引擎和加速平台。建立融合推理(Inference)、训练(Training)的软硬件一体的AI计算加速解决方案,并应用于行业最大的规模的AI数据中心,解决云计算、搜索、信息流、图像、视觉、语音、自然语言处理等的算法优化与计算加速问题。 -负责大规模AI前向计算引擎(Inference Engine)框架和底层算子开发与优化; -负责大规模AI计算通信库及通信算法开发与优化; -负责面向CPU/GPU/FPGA/ASIC等多元化计算架构的编译系统开发、编译优化和算法加速; -负责异构高性能计算平台的设计、研发,高性能计算库、通信库开发与优化。
包括英文材料
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.
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/
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。
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.
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.
LLVM+
https://llvm.org/docs/GettingStarted.html
Welcome to the LLVM project!
https://llvm.org/docs/tutorial/
This is the “Kaleidoscope” Language tutorial, showing how to implement a simple language using LLVM components in C++.
https://mcyoung.xyz/2023/08/01/llvm-ir/
“LLVM” is an umbrella name for a number of software components that can be used to build compilers.
https://www.youtube.com/watch?v=Lvc8qx8ukOI
This is the first lecture from the "Programming Language with LLVM" course where we build a full programming language similar to JavaScript from scratch, using LLVM compiler infrastructure.
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
内核+
https://www.youtube.com/watch?v=C43VxGZ_ugU
I rummage around the Linux kernel source and try to understand what makes computers do what they do.
https://www.youtube.com/watch?v=HNIg3TXfdX8&list=PLrGN1Qi7t67V-9uXzj4VSQCffntfvn42v
Learn how to develop your very own kernel from scratch in this programming series!
https://www.youtube.com/watch?v=JDfo2Lc7iLU
Denshi goes over a simple explanation of what computer kernels are and how they work, alonside what makes the Linux kernel any special.
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校招AIDU项目
我们致力于构建全球领先的AI异构计算加速引擎和加速平台。建立融合推理(Inference)、训练(Training)的软硬件一体的AI计算加速解决方案,并应用于行业最大的规模的AI数据中心,解决云计算、搜索、信息流、图像、视觉、语音、自然语言处理等的算法优化与计算加速问题。 -负责大规模AI前向计算引擎(Inference Engine)框架和底层算子开发与优化; -负责大规模AI计算通信库及通信算法开发与优化; -负责面向CPU/GPU/FPGA/ASIC等多元化计算架构的编译系统开发、编译优化和算法加速; -负责异构高性能计算平台的设计、研发,高性能计算库、通信库开发与优化。
更新于 2025-05-19
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-参与深度学习平台飞桨( PaddlePaddle)推理框架的设计、开发和业务支持 -负责深度学习框架的性能优化工作,包括但不限于功能模块在CPU/GPU上的深度优化工作 -负责深度学习推理框架前瞻技术的跟踪调研,实现技术创新突破 -参与深度学习框架的易用性优化工作,使开发者能够以更简单的方式实现各类任务,降低学习成本和开发成本 -负责异构高性能计算平台的设计、研发,高性能计算库、通信库开发与优化 -负责文心一言、萝卜快跑、搜索等业务大模型的推理性能优化
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-负责百度飞桨平台的智能体应用开发 -理解产品业务,结合业务场景需求,应用大模型、工具搭建平台产品的智能助手 -结合智能助手研发的过程,沉淀通用的开发工具 -解决自然语言处理核心技术在应用落地中的最后一公里问题
更新于 2024-03-14