阿里巴巴AI应用算法工程师
实习兼职阿里巴巴2027届实习生地点:北京 | 广州 | 杭州 | 上海状态:招聘
工作描述
任职要求 1.基础条件 ● 计算机、数学、统计学等相关专业硕士/博士优先,优秀本科生不受限制。 ● 有顶会论文(ACL/EMNLP/ICLR/NeurIPS/ICML等)/高影响项目/开源贡献者加分。 2.专业能力 ● 模型与后训练:解Transformer/LLM模型架构、演进原理与局限;具备SFT/DPO/RL等后训练与迭代能力(含数据/评估/优化),对后训练算法有实践经验和深刻认知;拥有Agentic RL训练实操经验者优先。 ● Agent与系统编排:能做任务拆解与多Agent协作;熟悉RAG、Memory、Tool-Use(含MCP/类协议/Skills等)并能工程化落地;独立开发过具备一定影响力AI应用者优先。 ● 数据构建:具备Data-centric AI意识,精通后训练所需高质量数据挖掘与构造,具备合成数据(Synthetic Data)与动作轨迹(Trajectory)构建实践经验者优先。 ● 评测与交付:能搭建评估闭环(autorater/LLM-as-judge、离线评测、A/B);具备测试、调试、review、安全/隐私与质量门禁能力;能基于评测结果精准分析和定位问题,具备复杂多步任务的量化评估能力者优先。 ● 工程…
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包括英文材料
ACL+
https://www.aclweb.org/portal/
Computational linguistics is the scientific study of language from a computational perspective.
EMNLP+
ICLR+
https://iclr.cc/
NeurIPS+
https://neurips.cc/
ICML+
https://icml.cc/
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
SFT+
https://cameronrwolfe.substack.com/p/understanding-and-using-supervised
Understanding how SFT works from the idea to a working implementation...
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/
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.
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