百度大模型算法工程师（J92880）
社招全职MEG地点：北京状态：招聘
任职要求
-有搜广推、大模型研发、智能体研发、RAG和强化学习等经验 -对机器学习、深度学习、自然语言处理和大模型有深刻的理解 -熟练掌握至少一种深度学习框架（PaddlePaddle、pytorch等） -熟练掌握Python/C++至少一门开发语言 -有机器学习、信息检索、自然语言处理和大模型等相关顶级会议论文者优先 -有强烈的上进心和求知欲，善于学习新事物，渴望用技术改变未来
工作职责
负责百度文心智能体策略优化，主要涉及到以下工作内容 -提示词工程、大模型定制调优等 -智能体理解评价、智能体内容生成质量评估、内容生成润色、多模态评估能力建设等 -基于大模型构建智能体在搜索场景下搜广推策略, 解决智能体分发中难点问题 -积极探索智能体在搜索场下新分发形态、新分发场景
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
智能体+
https://learn.microsoft.com/en-us/shows/ai-agents-for-beginners/
In this 10-lesson course we take you from concept to code while covering the fundamentals of building AI agents.
https://www.ibm.com/think/ai-agents
Your one-stop resource for gaining in-depth knowledge and hands-on applications of AI agents.
RAG+
https://www.youtube.com/watch?v=sVcwVQRHIc8
Learn how to implement RAG (Retrieval Augmented Generation) from scratch, straight from a LangChain software engineer.
强化学习+
https://cloud.google.com/discover/what-is-reinforcement-learning?hl=en
Reinforcement learning (RL) is a type of machine learning where an "agent" learns optimal behavior through interaction with its environment.
https://huggingface.co/learn/deep-rl-course/unit0/introduction
This course will teach you about Deep Reinforcement Learning from beginner to expert. It’s completely free and open-source!
https://www.kaggle.com/learn/intro-to-game-ai-and-reinforcement-learning
Build your own video game bots, using classic and cutting-edge algorithms.
机器学习+
https://www.youtube.com/watch?v=0oyDqO8PjIg
Learn about machine learning and AI with this comprehensive 11-hour course from @LunarTech_ai.
https://www.youtube.com/watch?v=i_LwzRVP7bg
Learn Machine Learning in a way that is accessible to absolute beginners.
https://www.youtube.com/watch?v=NWONeJKn6kc
Learn the theory and practical application of machine learning concepts in this comprehensive course for beginners.
https://www.youtube.com/watch?v=PcbuKRNtCUc
Learn about all the most important concepts and terms related to machine learning and AI.
深度学习+
https://d2l.ai/
Interactive deep learning book with code, math, and discussions.
NLP+
https://www.youtube.com/watch?v=fNxaJsNG3-s&list=PLQY2H8rRoyvzDbLUZkbudP-MFQZwNmU4S
Welcome to Zero to Hero for Natural Language Processing using TensorFlow!
https://www.youtube.com/watch?v=R-AG4-qZs1A&list=PLeo1K3hjS3uuvuAXhYjV2lMEShq2UYSwX
Natural Language Processing tutorial for beginners series in Python.
https://www.youtube.com/watch?v=rmVRLeJRkl4&list=PLoROMvodv4rMFqRtEuo6SGjY4XbRIVRd4
The foundations of the effective modern methods for deep learning applied to NLP.
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。
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.
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.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
信息检索+
https://nlp.stanford.edu/IR-book/information-retrieval-book.html
Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Introduction to Information Retrieval, Cambridge University Press. 2008.
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