高德地图高德-Java高级开发工程师/技术专家-交易策略
社招全职3年以上技术类-开发地点：北京状态：招聘
任职要求
注意：如候选人具备高匹配度交易策略场景开发经验，后端语言技术栈除Java可考虑golang或者C++ 1.要求3年以上Java/Golang开发经验；有出行业务经验、交易撮合、策略特征工程等研发经验者优先； 2.熟悉分布式系统的设计和应用，熟悉分布式、缓存、消息等机制；能对分布式常用技术进行合理应用，解决问题； 3.掌握OOD、DDD等系统设计方法论，具有较强的体系化、结构化思维能力，善于挖掘问题背后的本质，面对复杂系统、复杂问题有化繁为简的能力； 4.熟练掌握海量数据处理技术，有使用Hadoop、Hive、Spark、Flink分析海量数据的经验优先考虑； 5.具备数据敏感度，理解实验或大盘数据，并能从中发现关键问题；熟悉常用的机器学习算法，例如GBDT、LR、LTR，深度学习等，熟悉常用特征工程的方法，有实际数据挖掘、数据分析和特征工程经验可加分； 6.有技术热情和较强的学习能力，对于新技术有浓烈的好奇心，能够深入了解开源技术、现有系统技术等相关技术原理，有很好的问题分析和技术攻关能力，具有良好的团队合作能力、沟通能力、抗压能力。
工作职责
1.负责高德共享出行业务的架构重构和持续演进，供需实时数据系统、司机优选系统、指标特征系统等建设； 2.协同产品/算法分析各项策略效果指标，优化、调整策略方向及规则；建立体系化的实验、分析、优化、迭代的机制； 3.保障链路稳定性及高可用、高并发、高性能；
包括英文材料
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.
C+++
https://www.learncpp.com/
LearnCpp.com is a free website devoted to teaching you how to program in modern C++.
https://www.youtube.com/watch?v=ZzaPdXTrSb8
Go+
https://www.youtube.com/watch?v=8uiZC0l4Ajw
学习Golang的完整教程！从开始到结束不到一个小时，包括如何在Go中构建API的完整演示。没有多余的内容，只有你需要知道的知识。
特征工程+
https://www.ibm.com/think/topics/feature-engineering
Feature engineering preprocesses raw data into a machine-readable format. It optimizes ML model performance by transforming and selecting relevant features.
https://www.kaggle.com/learn/feature-engineering
Better features make better models. Discover how to get the most out of your data.
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
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
DDD+
https://ddd-crew.github.io/ddd-starter-modelling-process/
This process gives you a step-by-step guide for learning and practically applying each aspect of Domain-Driven Design (DDD) - from orienting around an organisation’s business model to coding a domain model.
[英文] Domain Driven Design
https://medium.com/@matteopampana/list/domain-driven-design-c1efaabe287e
Everyone talks about DDD, but how many understand and correctly apply Domain-Driven Design? I want to be one of them.
https://redis.io/glossary/domain-driven-design-ddd/
Domain-Driven Design (DDD) is a software development philosophy that emphasizes the importance of understanding and modeling the business domain.
系统设计+
https://roadmap.sh/system-design
Everything you need to know about designing large scale systems.
https://www.youtube.com/watch?v=F2FmTdLtb_4
This complete system design tutorial covers scalability, reliability, data handling, and high-level architecture with clear explanations, real-world examples, and practical strategies.
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
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.
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.
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.
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/
GBDT+
https://developers.google.com/machine-learning/decision-forests/intro-to-gbdt
Like bagging and boosting, gradient boosting is a methodology applied on top of another machine learning algorithm.
https://scikit-learn.org/stable/modules/ensemble.html
Ensemble methods combine the predictions of several base estimators built with a given learning algorithm in order to improve generalizability / robustness over a single estimator.
深度学习+
https://d2l.ai/
Interactive deep learning book with code, math, and discussions.
数据挖掘+
https://www.youtube.com/watch?v=-bSkREem8dM
Database vs Data Warehouse vs Data Lake
https://www.youtube.com/watch?v=7rs0i-9nOjo
数据分析+
[英文] Data Analyst Roadmap
https://roadmap.sh/data-analyst
Step by step guide to becoming an Data Analyst in 2025
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