理想汽车【自动驾驶】高级云端模型算法工程师
社招全职3年以上自动驾驶地点：北京状态：招聘
任职要求
1.计算机视觉、模式识别、机器学习、机器人，电子信息等相关专业的硕士/博士，3年以上工作经验。 2.有使用多模态大模型或LLM处理复杂问题的经验优先； 3.有自动驾驶数据闭环经验优先，包括但不限于数据挖掘、影子模型、多模态检索、自动标注， VLM 模型，数据合成算法， AI AGENT应用等； 4.熟悉数据结构、算法、并行编程和大规模数据处理等相关知识，至少精通Python或C/C++编程，有ACM经验者优先； 5.有计算机视觉及模型识别领域顶会（CVPR/ICCV/ECCV/ICML/NeurlIPS）或顶刊（TPAMI/IJCAI/TIP）者优先，有顶级学术比赛成果经验者优先。
工作职责
1.负责自动标注算法研发，实现多模态数据的联合生成与标注，涉及算法有点云分割&检测/动静态BEV/OCC/VLM等，支撑端到端/VLA项目落地； 2.负责云端VLM/VLA算法研发，并落地车云端； 3.负责重建生成算法在自动驾驶场景的研发，应用于静态标注和数据合成业务中； 4.探索新的模型训练方式在自动驾驶场景的落地，包括自监督/弱监督/增量训练/强化学习/数据配比方案等。 5.跟踪最新的大模型和人工智能发展动态，持续迭代更新多模态大模型方案； 6.主导关键技术的专利撰写和论文发表工作。
包括英文材料
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.
模式识别+
https://www.mathworks.com/discovery/pattern-recognition.html
Pattern recognition is the process of classifying input data into objects, classes, or categories using computer algorithms based on key features or regularities.
https://www.microsoft.com/en-us/research/wp-content/uploads/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf
Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science.
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.
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
自动驾驶+
https://www.youtube.com/watch?v=_q4WUxgwDeg&list=PL05umP7R6ij321zzKXK6XCQXAaaYjQbzr
Lecture: Self-Driving Cars (Prof. Andreas Geiger, University of Tübingen)
https://www.youtube.com/watch?v=NkI9ia2cLhc&list=PLB0Tybl0UNfYoJE7ZwsBQoDIG4YN9ptyY
You will learn to make a self-driving car simulation by implementing every component one by one. I will teach you how to implement the car driving mechanics, how to define the environment, how to simulate some sensors, how to detect collisions and how to make the car control itself using a neural network.
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
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/
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.
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
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.
C+
https://www.freecodecamp.org/chinese/news/the-c-beginners-handbook/
本手册遵循二八定律。你将在 20% 的时间内学习 80% 的 C 编程语言。
https://www.youtube.com/watch?v=87SH2Cn0s9A
https://www.youtube.com/watch?v=KJgsSFOSQv0
This course will give you a full introduction into all of the core concepts in the C programming language.
https://www.youtube.com/watch?v=PaPN51Mm5qQ
In this complete C programming course, Dr. Charles Severance (aka Dr. Chuck) will help you understand computer architecture and low-level programming with the help of the classic C Programming language book written by Brian Kernighan and Dennis Ritchie.
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
CVPR+
https://cvpr.thecvf.com/
ICCV+
https://iccv.thecvf.com/
ICCV is the premier international computer vision event comprising the main conference and several co-located workshops and tutorials.
ECCV+
https://eccv.ecva.net/
ECCV is the official event under the European Computer Vision Association and is biannual on even numbered years.
ICML+
https://icml.cc/
相关职位
社招3年以上自动驾驶
1. 负责理想汽车自动驾驶端到端模型方法研发和工程落地，包括但不限于动静态感知/通用障碍物/障碍物预测决策等端到端模型； 2. 负责设计高性能上限，具备量产能力的端到端模型算法，包括但不限于diffusion、VLM等模型算法； 3. 开发高效离线训练框架，以及可实时运行的在线推理框架，优化模型推理性能，研发模型部署工具链和优化工具； 4. 建立云端数据感知/决策联合标注Pipeline、数据挖掘机制以及难样本分析等工具链，利用影子模型挖掘众包数据，通过数据闭环持续选代模型能力。
社招自动驾驶
1.负责自动驾驶云端世界模型的研究、算法开发和交付落地； 2.分析自动驾驶对于世界模型的业务需求，建立评测方案和指标体系，确保技术方案可量产； 3.跟踪自动驾驶领域世界模型的前沿技术，探索新算法在实际场景中的应用，保持技术行业领先；
社招自动驾驶
1.负责研发和落实理想汽车下一代自动驾驶端到端VLA大模型算法，确保在车载和云端平台的成功部署。 2.专注于端到端大模型自动驾驶系统的算法开发和优化，包括但不限于端到端模型、多模态大模型等领域。 3.参与大规模自动驾驶数据集的处理、标注及管理，优化大模型以提升自动驾驶系统的性能。 4.持续关注并跟踪自动驾驶及人工智能领域的最新技术进展，进行技术调研及新技术的原型验证。
社招3年以上自动驾驶
1.负责理想汽车自动驾驶端到端模型方法研发和工程落地，包活动静态感知/通用障碍物/障碍物预测决策等端到端模型; 2.开发高效离线训练框架，以及可实时运行的在线推理框架，优化模型推理性能，研发模型部署工具链和优化工具； 3.建立云端数据感知/决策联合标注Pipeline、数据挖掘机制以及难样本分析等工具链，利用影子模型挖掘众包数据，通过数据闭环持续选代模型能力。