
地平线【地瓜机器人】算法工程化|机器人AI平台研发工程师
校招全职软件序列地点:北京状态:招聘
工作描述
任职要求 必备条件 1. 应届生(2027 届),计算机、自动化、电子、人工智能、软件、数学等相关专业优先; 2. 扎实的算法与工程基础:熟悉机器学习/深度学习原理,能独立完成从数据到模型的完整闭环; 3. 较强的理解力:能快速抓住问题本质,准确复述并拆解技术需求; 4. 较强的表达力:书面与口头表达清晰、结构化,能把复杂技术问题讲清楚; 5. 较强的逻辑与辩证思维:能把问题、方案、实验、结论拆成有结构的链条,且能区分不同维度、避免非黑即白; 6. 信息甄别与时效意识:面对海量或冲突的信息,能判断来源可信度、时效性,并快速定位关键变量; 7. AI 工具使用能力:能熟练用 AI 辅助工作,并有验证和甄别 AI 输出的意识; 8. 对技术有热情,能快速理解新概念并翻译成可落地的方案。 我们希望你是什么样的人 我们不找"空心人"。比起你已经会什么,我们更看重你是什么样的人: - 能抓住本质:接触新东西时,先问"它解决什么问题、代价是什么、边界在哪",而不是先问"怎么用"。 - 能拆维度:遇到"A 和 B 是不是矛盾"这类问题,第一反应是把它拆成几个不同的维度分别看,而不是急着站队下结论。 - 认知潜力大于技术堆叠:学习快、迁移强,能从一次实验、一个 bug、一次复盘里提炼出可复用的方法论,而不只是"这次解决了"。我们在意你把一件事想明白的速度,远多于你已经会多少工具。 - 爱折腾、有好奇心:有 side project、折腾过奇怪的东西、对新鲜事物有本能的兴趣;真正碰过设备、写过驱动、联过系统的人,比只看过论文的人更懂真实世界的问题。 - 靠谱且自驱:对结果负责,遇到问题先求证再下结论;能把"问题→方案→实验→结论"讲成一条清晰的因果链。 - 会用 AI 而非依赖 AI:给足上下文、会验证、敢让 AI 反驳自己。知识大模型都有,你的价值在判断与拆解。 成长路径 - 参与真实平台算法能力从 0 到 1 的完整过程; - 深入具身智能这一前沿赛道,建立技术认知与判断力; - 培养算法 + 工程 + 商业化落地的复合能力,为向技术专家/架构方向发展打基础。 工作职责 关于我们 我们正在做具身智能/机器人开发平台,专注于机器人基础设施建设,为企业、算法工…
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包括英文材料
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/
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.
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
OpenCV+
https://learnopencv.com/getting-started-with-opencv/
At LearnOpenCV we are on a mission to educate the global workforce in computer vision and AI.
https://opencv.org/university/free-opencv-course/
This free OpenCV course will teach you how to manipulate images and videos, and detect objects and faces, among other exciting topics in just about 3 hours.
AI agent+
https://www.ibm.com/think/ai-agents
Your one-stop resource for gaining in-depth knowledge and hands-on applications of AI agents.
SLAM+
https://docs.mrpt.org/reference/latest/tutorial-slam-for-beginners-the-basics.html
[英文] SLAM for Dummies
https://dspace.mit.edu/bitstream/handle/1721.1/119149/16-412j-spring-2005/contents/projects/1aslam_blas_repo.pdf
A Tutorial Approach to Simultaneous Localization and Mapping
https://ouster.com/insights/blog/introduction-to-slam-simultaneous-localization-and-mapping
SLAM is an essential piece in robotics that helps robots to estimate their pose – the position and orientation – on the map while creating the map of the environment to carry out autonomous activities.
[英文] What Is SLAM?
https://www.mathworks.com/discovery/slam.html
How it works, types of SLAM algorithms, and getting started
SDK+
https://www.ibm.com/think/topics/api-vs-sdk
Learn about software development kits (SDKs) and application programming interfaces (APIs) and how they improve both software development cycles and the end-user experience (UX).
https://www.redhat.com/zh-cn/topics/cloud-native-apps/what-is-SDK
软件开发套件(SDK)是通常由硬件平台、操作系统(OS)或编程语言的制造商提供的一套工具。
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.
Gymnasium+
https://gymnasium.farama.org/index.html
An API standard for reinforcement learning with a diverse collection of reference environments
https://www.youtube.com/watch?v=FvuyrpzvwdI
Learn to use Gymnasium for Python, which allows you to create environments to run reinforcement learning programs against in Python.
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社招3年以上软件序列
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校招算法序列
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