阿里云阿里云智能-可观测智能算法专家-杭州
社招全职5年以上云智能集团地点:杭州状态:招聘
任职要求
1.专业背景扎实,计算机、人工智能、软件工程、模式识别、统计学等相关专业; 2.具备扎实的算法功底,精通机器学习/深度学习算法基础,尤其在时序分析、异常检测、因果推断、图算法(GNN)、强化学习等领域有深入研究或实践经验者优先; 3.熟练掌握 LLM 技术能力,熟悉 LLM 主流算法原理,在 Fine-tuning、Prompt Engineering、RAG、Agentic 应用开发等一个或多个方向有深入的实践经验。主导过有影响力的大模型相关项目或在顶级会议/期刊发表过相关论文者优先; 4…
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工作职责
负责阿里云可观测平台核心算法与智能引擎建设,沉淀深厚的运维领域知识与经验。加入该岗位,您将基于大模型技术,构建下一代 AIOps 产品核心竞争力,打造面向未来的智能运维基础设施。 1. 研发可观测核心算子,负责设计与研发面向海量可观测数据的核心算法算子,实现对海量原始数据的高效预处理与特征提取,为上层智能应用提供高质量输入; 2.参与 AIOps Agent 设计与研发,负责 LLM 驱动的 AIOps Agent 的核心算法研发。通过多 Agent 架构解决复杂场景下的根因定位、影响评估、智能巡检、辅助运维等难题; 3.构建并应用 AIOps Benchmark 体系,设计和落地具有业界影响力的 AIOps Benchmark 评测体系。通过系统化的故障注入与案例复盘,构建覆盖广泛、高度真实的评测数据集,用于度量和持续优化 AIOps 系统的泛化能力; 4.探索前沿模型训练与优化技术,运用监督微调(SFT)、强化学习(RLHF)等前沿技术,针对 AIOps 中的关键过程进行模型优化和迭代,持续提升 AIOps 的准确性和性能; 5.追踪前沿并推动技术落地,持续追踪和研究 LLM、Agent、知识图谱、图神经网络等技术在 AIOps 领域的最新进展,结合阿里云可观测产品的实际场景,探索和推动前沿技术的应用与落地,构建技术壁垒。
包括英文材料
模式识别+
https://www.mathworks.com/discovery/pattern-recognition.html
Pattern recognition is the process of classifying input data into objects, classes, or categories using computer algorithms based on key features or regularities.
https://www.microsoft.com/en-us/research/wp-content/uploads/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf
Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science.
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.
因果推断+
https://web.stanford.edu/~swager/causal_inf_book.pdf
How best to understand and characterize causality is an age-old question in philosophy.
GNN+
https://distill.pub/2021/gnn-intro/
Neural networks have been adapted to leverage the structure and properties of graphs.
https://gnn.seas.upenn.edu/
Graph Neural Networks (GNNs) are information processing architectures for signals supported on graphs.
https://www.ibm.com/think/topics/graph-neural-network
Graph neural networks (GNNs) are a deep neural network architecture that is popular both in practical applications and cutting-edge machine learning research.
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