高德地图高德-NLP/Agent算法工程师/专家-POI智能化
社招全职3年以上技术类-算法地点:北京状态:招聘
工作描述
Qualifications 熟悉 NLP 的各种任务建模,并且有丰富的实践经验,包含但不仅限于切词、NER、语言模型、事件检测与要素抽取、分类等;深入理解 Transformer 架构及主流 LLM(如 Qwen, Llama 等)的原理与应用; 熟悉主流Agent框架(如LangGraph,Hermes Agent,AgentScope,Anthropic Agent等),熟练掌握session、memory、rag、prompting、cache、tool using、skill和mcp等技术,有LLM训练经验和分布式应用经验; 有机器人智能语音外呼、移动设备agent、智能决策和审核agent…
登录查看完整工作描述
微信扫码,1秒登录
包括英文材料
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.
Transformer+
https://huggingface.co/learn/llm-course/en/chapter1/4
Breaking down how Large Language Models work, visualizing how data flows through.
https://poloclub.github.io/transformer-explainer/
An interactive visualization tool showing you how transformer models work in large language models (LLM) like GPT.
https://www.youtube.com/watch?v=wjZofJX0v4M
Breaking down how Large Language Models work, visualizing how data flows through.
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
Llama+
https://github.com/LlamaFamily/Llama-Chinese
Llama中文社区,实时汇总最新Llama学习资料,构建最好的中文Llama大模型开源生态,完全开源可商用。
https://www.llama.com/docs/overview/
This guide provides information and resources to help you set up Llama including how to access the model, hosting, how-to and integration guides.
AI agent+
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.
缓存+
https://hackernoon.com/the-system-design-cheat-sheet-cache
The cache is a layer that stores a subset of data, typically the most frequently accessed or essential information, in a location quicker to access than its primary storage location.
https://www.youtube.com/watch?v=bP4BeUjNkXc
Caching strategies, Distributed Caching, Eviction Policies, Write-Through Cache and Least Recently Used (LRU) cache are all important terms when it comes to designing an efficient system with a caching layer.
https://www.youtube.com/watch?v=dGAgxozNWFE
还有更多 •••
相关职位
社招2年以上核心本地商业-基
1. 计算机、数学等相关专业本科及以上学历,2-5 年大模型或NLP相关算法经验。 2. 熟悉大模型的后训练范式,包括高效微调、微调、强化学习等。 3. 深刻理解RAG机制,在 Query 改写、
更新于 2026-07-15北京|上海
实习阿里巴巴研究型实
Qualifications 熟悉 NLP 的各种任务建模,并且有丰富的实践经验,包含但不仅限于切词、NER、语言模型、事件检测与要素抽取、分类等;深入理解 Transformer 架构及主流 LLM
更新于 2026-08-10北京
校招技术开发类
1、2022年应届毕业生,硕士学历; 2、计算机、数学、统计相关专业; 3、具有python、C++等语言程序设计能力; 4、熟悉CRF、SVM、RNN等文本处理算法原理; 5、熟悉word2vec、
更新于 2022-08-08苏州