米哈游资深数据系统研发工程师(舆情平台方向)
社招全职5年以上程序&技术类地点:上海状态:招聘
任职要求
1、计算机、软件工程或相关专业本科及以上学历,5年以上后端或数据平台研发经验,其中至少2年涉及与算法团队紧密协作的工程项目,如搜索推荐、内容理解、舆情分析或大规模文本处理系统。 2、精通Java及主流微服务框架,具备构建高可用、高性能分布式系统的丰富经验。 3、熟练掌握实时数据流处理技术(如Flink、Spark Streaming),并有在复杂业务场景下的开发与调优经验。 4、熟悉大数据生态(Hadoop, Hive, Kafka等),精通至少一种OLAP引擎(如ClickHouse, Doris)用于多维分析与即席查询。 4、具备扎实的算法工程化经验,深入理解常见NLP/机器学习模型(如分类、聚类、NER)的线上服务部署、性能优化与效果评估流程。熟悉模型服务化(Model Serving)相关框架与…
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工作职责
负责舆情监测与分析平台的工程架构与核心系统研发。您将主导构建融合算法能力的舆情数据管道、实时分析引擎与智能处理平台、情感分析、事件发现与深度洞察,为公司的品牌管理、市场决策和用户洞察提供关键支持。 1、负责舆情平台后端系统的整体架构与研发,核心覆盖舆情数据采集引擎、实时流处理管道、舆情智能分析引擎(集成情感分析、主题聚类、事件检测、实体识别等算法模型)以及数据服务接口。 2、设计和实现高吞吐、低延迟的舆情数据处理链路,能够高效处理海量异构文本、视频、音频等多模态数据,并为算法模型提供稳定的数据供给与计算环境。 3、与算法团队深度协作,负责将NLP、机器学习及大语言模型(LLM)等算法能力(如文本分类、情感判定、摘要生成、观点抽取、趋势预测)进行工程化落地、服务封装与性能优化,并集成到舆情分析产品中。 4、构建稳定、易用的舆情数据服务与API,支撑舆情实时预警、智能仪表盘、自动报告生成等产品功能,赋能业务部门实现数据驱动的决策。 5、负责舆情平台基础设施的稳定性、成本优化与全链路性能调优,确保7x24小时可靠服务。
包括英文材料
学历+
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/
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.
微服务+
https://learn.microsoft.com/en-us/training/modules/dotnet-microservices/
Microservice applications are composed of small, independently versioned, and scalable customer-focused services that communicate with each other by using standard protocols and well-defined interfaces.
https://microservices.io/
Microservices - also known as the microservice architecture - is an architectural style that structures an application as a collection of two or more services.
https://spring.io/microservices
Building small, self-contained, ready to run applications can bring great flexibility and added resilience to your code.
https://www.ibm.com/think/topics/microservices
Microservices, or microservices architecture, is a cloud-native architectural approach in which a single application is composed of many loosely coupled and independently deployable smaller components or services.
https://www.youtube.com/watch?v=CqCDOosvZIk
https://www.youtube.com/watch?v=hmkF77F9TLw
Learn about software system design and microservices.
高可用+
https://redis.io/blog/high-availability-architecture/
A high available architecture is when there are a number of different components, modules, or services that work together to maintain optimal performance, irrespective of peak-time loads.
https://www.ibm.com/think/topics/high-availability
High availability (HA) is a term that refers to a system’s ability to be accessible and reliable close to 100% of the time.
分布式系统+
https://www.distributedsystemscourse.com/
The home page of a free online class in distributed systems.
https://www.youtube.com/watch?v=7VbL89mKK3M&list=PLOE1GTZ5ouRPbpTnrZ3Wqjamfwn_Q5Y9A
Flink+
https://nightlies.apache.org/flink/flink-docs-release-2.0/docs/learn-flink/overview/
This training presents an introduction to Apache Flink that includes just enough to get you started writing scalable streaming ETL, analytics, and event-driven applications, while leaving out a lot of (ultimately important) details.
https://www.youtube.com/watch?v=WajYe9iA2Uk&list=PLa7VYi0yPIH2GTo3vRtX8w9tgNTTyYSux
Today’s businesses are increasingly software-defined, and their business processes are being automated. Whether it’s orders and shipments, or downloads and clicks, business events can always be streamed. Flink can be used to manipulate, process, and react to these streaming events as they occur.
Spark+
[英文] Learning Spark Book
https://pages.databricks.com/rs/094-YMS-629/images/LearningSpark2.0.pdf
This new edition has been updated to reflect Apache Spark’s evolution through Spark 2.x and Spark 3.0, including its expanded ecosystem of built-in and external data sources, machine learning, and streaming technologies with which Spark is tightly integrated.
大数据+
https://www.youtube.com/watch?v=bAyrObl7TYE
https://www.youtube.com/watch?v=H4bf_uuMC-g
With all this talk of Big Data, we got Rebecca Tickle to explain just what makes data into Big Data.
Hadoop+
https://www.runoob.com/w3cnote/hadoop-tutorial.html
Hadoop 为庞大的计算机集群提供可靠的、可伸缩的应用层计算和存储支持,它允许使用简单的编程模型跨计算机群集分布式处理大型数据集,并且支持在单台计算机到几千台计算机之间进行扩展。
[英文] Hadoop Tutorial
https://www.tutorialspoint.com/hadoop/index.htm
Hadoop is an open-source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models.
Hive+
[英文] Hive Tutorial
https://www.tutorialspoint.com/hive/index.htm
Hive is a data warehouse infrastructure tool to process structured data in Hadoop. It resides on top of Hadoop to summarize Big Data, and makes querying and analyzing easy.
https://www.youtube.com/watch?v=D4HqQ8-Ja9Y
Kafka+
https://developer.confluent.io/what-is-apache-kafka/
https://www.youtube.com/watch?v=CU44hKLMg7k
https://www.youtube.com/watch?v=j4bqyAMMb7o&list=PLa7VYi0yPIH0KbnJQcMv5N9iW8HkZHztH
In this Apache Kafka fundamentals course, we introduce you to the basic Apache Kafka elements and APIs, as well as the broader Kafka ecosystem.
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