美团大模型应用研发工程师
社招全职核心本地商业-点评事业部地点:上海状态:招聘
任职要求
1. 对大模型及其产品的应用和开发有浓厚的兴趣; 2. 具有优秀的代码能力、扎实的Java功底,有较好的IO、多线程、网络等方面的编程能力; 3. 熟悉大模型结构,了解大模型算法、模型蒸馏、稀疏化、剪裁等技术; 4. 有较强的逻辑/概率思维能力和业务/数据敏感度,善于定义、分析、归纳、解决问题; 5. 具备强烈的工作责任心、技术投入度和良好的团队协作能力,能持续学习和总结,自我迭代。 具备以下条件优先 1. 有大模型应用开发落地经验,或有机器学习、深度学习以及自然语言处理算法的基础知识及相关工程经验者优先; 2. 有模型训练或推理部署经验,熟练使用Python,熟悉 PyTorch 或 TensorFlow,了解 Triton 推理框架者优先;
工作职责
1. 负责大模型驱动的AI产品的工程化落地,支持AI找店、内容生成等业务场景; 2. 与大模型应用算法团队紧密协作,探索预训练、微调、评估、多模态、安全等前沿技术问题; 3. 设计并实现可扩展的 LLM 应用架构,优化端到端性能和资源能效,不断提升在线系统的扩展性和稳定性。
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
Java+
https://www.youtube.com/watch?v=eIrMbAQSU34
Master Java – a must-have language for software development, Android apps, and more! ☕️ This beginner-friendly course takes you from basics to real coding skills.
多线程+
https://liaoxuefeng.com/books/java/threading/basic/index.html
和单线程相比,多线程编程的特点在于:多线程经常需要读写共享数据,并且需要同步。
https://www.youtube.com/watch?v=_uQgGS_VIXM&list=PLsc-VaxfZl4do3Etp_xQ0aQBoC-x5BIgJ
https://www.youtube.com/watch?v=IEEhzQoKtQU
https://www.youtube.com/watch?v=mTGdtC9f4EU&list=PLL8woMHwr36EDxjUoCzboZjedsnhLP1j4
https://www.youtube.com/watch?v=TPVH_coGAQs&list=PLk6CEY9XxSIAeK-EAh3hB4fgNvYkYmghp
https://www.youtube.com/watch?v=xPqnoB2hjjA
This video is an introduction to multithreading in modern C++.
https://www.youtube.com/watch?v=YKBwKy5PrpQ
Rust threading is easy to implement and improves the efficiency of your applications on multi-core systems!
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/
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.
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.
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.
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