小红书数据引擎Agent / AIOps 专家
社招全职3-5年数据引擎地点:北京 | 上海 | 杭州状态:招聘
任职要求
1.计算机相关专业,研究生学历,本科211以上 2.有大数据和技术风险领域的经验,深入原理并有相关场景的大规模实践 3.熟悉机器学习/深度学习算法(如 LSTM、GNN、异常检测算法等),熟练掌握数据ETL流程、PyTorch / TensorFlow 及 MLOps 生产工具链 4.熟悉并落地如下1个或多个领域的经验: a). 大规模云平台的资源分配、调度优化和中长期资源规划:运用需求预测、运筹优…
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工作职责
1.探索和落地大数据领域Agent和AIOps技术风险领域的前沿技术和应用场景,包括智能问答、推理分析、容量规划、数据治理、业务诊断、风险预测等,并将研究结果应用到数据平台和数据业务领域,不断推动服务能力升级。 2.整合多源异构数据来源,构建数据基座,结合传统AI算法和LLM,设计和实现Agent或传统AIOps架构。 3.解决算法工程化的问题,包括端到端应用算法解决方案、模型优化和在线模型更新、场景仿真实验和调优等。不断提升各应用场景的召回率和准确率。
包括英文材料
学历+
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.
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/
LSTM+
https://colah.github.io/posts/2015-08-Understanding-LSTMs/
Humans don’t start their thinking from scratch every second.
https://d2l.ai/chapter_recurrent-modern/lstm.html
The term “long short-term memory” comes from the following intuition.
https://developer.nvidia.com/discover/lstm
A Long short-term memory (LSTM) is a type of Recurrent Neural Network specially designed to prevent the neural network output for a given input from either decaying or exploding as it cycles through the feedback loops.
https://www.youtube.com/watch?v=YCzL96nL7j0
Basic recurrent neural networks are great, because they can handle different amounts of sequential data, but even relatively small sequences of data can make them difficult to train.
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.
ETL+
https://www.ibm.com/think/topics/etl
ETL—meaning extract, transform, load—is a data integration process that combines, cleans and organizes data from multiple sources into a single, consistent data set for storage in a data warehouse, data lake or other target system.
https://www.youtube.com/watch?v=OW5OgsLpDCQ
It explains what ETL is and what it can do for you to improve your data analysis and productivity.
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