高德地图高德-打车算法专家/高级算法工程师-营销/推荐/大模型
社招全职2年以上技术类-算法地点：北京状态：招聘
工作描述
任职要求 项目经历 ● 硕士及以上学历，计算机科学/人工智能/统计学/数学/电子工程/自动化等相关专业； ● 2 年以上相关领域（推荐/广告/营销/大模型/增长）算法实战经验； ● 有出行、本地生活或交易类业务背景者优先。 专业技能 ● 算法：扎实的数据结构、机器学习、运筹优化基础。熟悉树模型、深度学习、因果推断、强化学习、多模态大模型等方法。 ● AI能力：有大型 LLM 在核心业务场景的端到端落地实战经验。能独立将 AI 工具深度融入算法建模与工程流程，包括但不限于自动化建模、Prompt 驱动的数据分析与归因、模型效果自动诊断等。 ● 工程能力： 精通 Python，熟练使用 C++/Go 至少一种；熟练使用 PyTorch/Te…
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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/
数据结构+
https://www.youtube.com/watch?v=8hly31xKli0
In this course you will learn about algorithms and data structures, two of the fundamental topics in computer science.
https://www.youtube.com/watch?v=B31LgI4Y4DQ
Learn about data structures in this comprehensive course. We will be implementing these data structures in C or C++.
https://www.youtube.com/watch?v=CBYHwZcbD-s
Data Structures and Algorithms full course tutorial java
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.
运筹优化+
https://medium.com/gousto-engineering-techbrunch/an-introduction-to-operations-research-5a9e898b6c60
Operations research (OR) is a scientific approach to determining the optimal solution to a defined business problem.
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.
强化学习+
https://cloud.google.com/discover/what-is-reinforcement-learning?hl=en
Reinforcement learning (RL) is a type of machine learning where an "agent" learns optimal behavior through interaction with its environment.
https://huggingface.co/learn/deep-rl-course/unit0/introduction
This course will teach you about Deep Reinforcement Learning from beginner to expert. It’s completely free and open-source!
https://www.kaggle.com/learn/intro-to-game-ai-and-reinforcement-learning
Build your own video game bots, using classic and cutting-edge algorithms.
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
Python+
https://liaoxuefeng.com/books/python/introduction/index.html
中文，免费，零起点，完整示例，基于最新的Python 3版本。
https://www.learnpython.org/
a free interactive Python tutorial for people who want to learn Python, fast.
https://www.youtube.com/watch?v=K5KVEU3aaeQ
Master Python from scratch 🚀 No fluff—just clear, practical coding skills to kickstart your journey!
https://www.youtube.com/watch?v=rfscVS0vtbw
This course will give you a full introduction into all of the core concepts in python.
Scikit-learn+
https://www.ibm.com/think/topics/scikit-learn
Scikit-learn, or sklearn, is an open source project and one of the most used machine learning (ML) libraries today.
https://www.youtube.com/watch?v=SIEaLBXr0rk
Today we to a crash course on Scikit-Learn, the go-to library in Python when it comes to traditional machine learning algorithms (i.e., not deep learning).
TensorFlow+
https://www.youtube.com/watch?v=tpCFfeUEGs8
Ready to learn the fundamentals of TensorFlow and deep learning with Python? Well, you’ve come to the right place.
https://www.youtube.com/watch?v=ZUKz4125WNI
This part continues right where part one left off so get that Google Colab window open and get ready to write plenty more TensorFlow code.
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