高德地图算法工程师-大模型方向
实习兼职高德研究型实习生地点：北京状态：招聘
任职要求
1、博士学历及以上，熟练掌握机器学习、深度学习算法，包括不限于（Transformer，CNN，GNN，迁移学习，多模态等）或自然语言处理任务（文本相似度/深度学习算法）等原理； 2、具备大模型应用经验（Prompt 、SFT、RLHF等）经验，熟悉大模型/多模态等基础原理及应用思路优先考虑； 3、愿意在地图领域深耕，具有优秀的分析问题和解决问题的能力，对解决挑战性问题充满激情，具有良好的沟通能力，重视团队合作； 4、有ACM竞赛获奖/论文/顶会经验者优先。
工作职责
1、负责将深度学习、多模态大模型等技术与地图专业领域知识结合； 2、参与最前沿的生成式建图等领域模型研发，结合SFT/RLHF/RAG方向的前沿算法持续提升业务效果天花板； 3、负责大模型在地图特征提取和数据生成应用落地，包括系统性掌握Prompt工程的相关技术，与工程同学配合，完善整体链路，推进应用上线。
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/
Transformer+
https://huggingface.co/learn/llm-course/en/chapter1/4
Breaking down how Large Language Models work, visualizing how data flows through.
https://poloclub.github.io/transformer-explainer/
An interactive visualization tool showing you how transformer models work in large language models (LLM) like GPT.
https://www.youtube.com/watch?v=wjZofJX0v4M
Breaking down how Large Language Models work, visualizing how data flows through.
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.
NLP+
https://www.youtube.com/watch?v=fNxaJsNG3-s&list=PLQY2H8rRoyvzDbLUZkbudP-MFQZwNmU4S
Welcome to Zero to Hero for Natural Language Processing using TensorFlow!
https://www.youtube.com/watch?v=R-AG4-qZs1A&list=PLeo1K3hjS3uuvuAXhYjV2lMEShq2UYSwX
Natural Language Processing tutorial for beginners series in Python.
https://www.youtube.com/watch?v=rmVRLeJRkl4&list=PLoROMvodv4rMFqRtEuo6SGjY4XbRIVRd4
The foundations of the effective modern methods for deep learning applied to NLP.
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
Prompt+
https://cloud.google.com/vertex-ai/generative-ai/docs/learn/prompts/introduction-prompt-design
A prompt is a natural language request submitted to a language model to receive a response back.
https://learn.microsoft.com/en-us/azure/ai-foundry/openai/concepts/prompt-engineering
These techniques aren't recommended for reasoning models like gpt-5 and o-series models.
https://www.youtube.com/watch?v=LWiMwhDZ9as
Learn and master the fundamentals of Prompt Engineering and LLMs with this 5-HOUR Prompt Engineering Crash Course!
SFT+
https://cameronrwolfe.substack.com/p/understanding-and-using-supervised
Understanding how SFT works from the idea to a working implementation...
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