小米小米汽车-工业工程-智能制造AI算法工程师
社招全职5年以上A01417A地点：北京状态：招聘
任职要求
1. 学历与专业：计算机科学、人工智能、机器学习、数据分析或相关领域硕士及以上学位，且熟悉汽车制造流程 2. 能力要求： - 具备5年以上工业智能系统开发经验，主导过不少于5个工业级AI项目规划&实施&落地&维护与优化，深度理解MES等制造业标准体系，熟悉OPC-UA、Modbus等工业协议对接，有国内头部车企生产制造项目经验者优先 - 开发能力以Python（NumPy/Pandas/Scikit-learn/TensorFlow/PyTorch等机器学习库）为核心，熟悉Java/Scala - 掌握监督学习、无监督学习、强化学习及深度学习等机器学习算法的底层原理与调优策略，具有DeepSeek、ChatGPT等大模型的微调及智能体开发经验，熟悉Neo4j图数据库技术，具备工艺知识建模与推理应用实战能力 - 精通AI视觉技术，涵盖传统图像处理与深度学习模型，具备工业质检、工艺缺陷检测等场景落地项目 - 擅长时序数据分析：精通LSTM/DTW等算法，可完成生产设备预测性维护、工艺参数趋势预测等时序建模任务 - 掌握ETL全流程开发，熟悉特征工程、模型评估及数据可
工作职责
1. 深入了解汽车制造流程，系统化梳理整车生产全流程（冲压/压铸/焊装/涂装/总装/电池/电驱），识别AI技术在整车制造业务场景，输出可落地的智能化改造解决方案 2. 基于整车制造场景，牵头设计并实现基于AI大模型的智能体架构，构建覆盖工艺优化、知识管理、智能决策的标准化AI产品体系 3. 负责搭建支持AI模型开发、数据管理、服务部署的一体化平台，建立汽车制造相关AI数据库，实现算法模型从训练优化到生产部署的全生命周期管理 4. 负责实施和优化机器学习代码，用于数据预处理、特征工程、模型训练和评估，验证模型性能并迭代改进 5. 牵头制造版块人工智能技术研发路径拆解，开展汽车制造领域前沿技术研究，制定整车智能制造3-5年AI技术发展规划，支持降本、提质、增效业务目标达成 6. 牵头内部赋能培训计划，打造具备AI思维与实施能力的复合型人才梯队
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.
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
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.
NumPy+
https://numpy.org/doc/stable/user/absolute_beginners.html
NumPy (Numerical Python) is an open source Python library that’s widely used in science and engineering.
[英文] NumPy - Learn
https://numpy.org/learn/
Below is a curated collection of educational resources, both for self-learning and teaching others, developed by NumPy contributors and vetted by the community.
https://www.kaggle.com/code/themlphdstudent/learn-numpy-numpy-50-exercises-and-solution
This kernel uses exercises of NumPy from the Machine Learning Plus webpage
https://www.youtube.com/watch?v=KHoEbRH46Zk
If you've heard of Pandas and NumPy, you may think one is simply a superset of the other.
https://www.youtube.com/watch?v=QUT1VHiLmmI
Learn the basics of the NumPy library in this tutorial for beginners.
https://www.youtube.com/watch?v=VXU4LSAQDSc
This video serves as an introduction to the NumPy Python library.
Pandas+
[英文] 10 minutes to pandas
https://pandas.pydata.org/docs/user_guide/10min.html
This is a short introduction to pandas, geared mainly for new users.
[英文] Cookbook - pandas
https://pandas.pydata.org/docs/user_guide/cookbook.html#cookbook
This is a repository for short and sweet examples and links for useful pandas recipes.
https://www.kaggle.com/learn/pandas
Solve short hands-on challenges to perfect your data manipulation skills.
https://www.youtube.com/watch?v=2uvysYbKdjM
I'm super excited for this one. We're doing another complete Python Pandas tutorial walkthrough.
https://www.youtube.com/watch?v=Mdq1WWSdUtw
Filtering, Joins, Indexing, Data Cleaning, Visualizations
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.
PyTorch+
https://datawhalechina.github.io/thorough-pytorch/
PyTorch是利用深度学习进行数据科学研究的重要工具，在灵活性、可读性和性能上都具备相当的优势，近年来已成为学术界实现深度学习算法最常用的框架。
https://www.youtube.com/watch?v=V_xro1bcAuA
Learn PyTorch for deep learning in this comprehensive course for beginners. PyTorch is a machine learning framework written in Python.
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.
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.
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/
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
Neo4j+
https://graphacademy.neo4j.com/
We're here to guide you on a fun and engaging journey to mastering Neo4j with free, hands-on courses.
图像处理+
https://opencv.org/blog/computer-vision-and-image-processing/
This fascinating journey involves two key fields: Computer Vision and Image Processing.
https://www.geeksforgeeks.org/python/image-processing-in-python/
Image processing involves analyzing and modifying digital images using computer algorithms.
https://www.youtube.com/watch?v=kSqxn6zGE0c
In this Introduction to Image Processing with Python, kaggle grandmaster Rob Mulla shows how to work with image data in python!
LSTM+
https://colah.github.io/posts/2015-08-Understanding-LSTMs/
Humans don’t start their thinking from scratch every second.
https://d2l.ai/chapter_recurrent-modern/lstm.html
The term “long short-term memory” comes from the following intuition.
https://developer.nvidia.com/discover/lstm
A Long short-term memory (LSTM) is a type of Recurrent Neural Network specially designed to prevent the neural network output for a given input from either decaying or exploding as it cycles through the feedback loops.
https://www.youtube.com/watch?v=YCzL96nL7j0
Basic recurrent neural networks are great, because they can handle different amounts of sequential data, but even relatively small sequences of data can make them difficult to train.
ETL+
https://www.ibm.com/think/topics/etl
ETL—meaning extract, transform, load—is a data integration process that combines, cleans and organizes data from multiple sources into a single, consistent data set for storage in a data warehouse, data lake or other target system.
https://www.youtube.com/watch?v=OW5OgsLpDCQ
It explains what ETL is and what it can do for you to improve your data analysis and productivity.
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.
相关职位
校招
1. 负责3C智能制造领域的前沿算法研究； 2. 设计并实现创新的AI解决方案； 3. 与产品团队紧密合作，将研究成果转化为实际应用； 4. 撰写技术专利和学术论文； 5. 参与行业技术交流，跟踪最新技术发展。 【课题名称】 3C智能制造工业算法研究 【课题内容】 1. 机器学习与深度学习：重点研究工艺参数调优、设备预测性维护、生产质量预测与控制以及智能调度优化等关键技术，通过深度学习技术提升制造过程的智能化水平和效率； 2. 大模型应用：探索工业知识库构建、多模态工业数据理解、工业决策支持系统等前沿领域，利用大模型技术赋能工业智能化升级； 3. 具身智能：研究工业装备智能控制、自适应制造系统、人机协作系统等方向，推动智能装备向更高水平的自主化和智能化发展。
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校招管培生
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更新于 2025-10-17
校招智能制造
THE ROLE 作为一名物流仿真工程师，你需要使用离散事件仿真技术，构建真实物流系统的虚拟模型，通过仿真试验分析与优化，支撑规划实现更可靠更精益的生产与物流系统，同时，通过提升模块化建模水平不断提高仿真效率与现实还原度。这里还提供了仿真与算法等技术融合的创新平台，当你深入复杂规划场景的业务时，可以在平台上大胆尝试不同算法求解方案，逐步成长为物流仿真融合算法的复合人才。 RESPONSIBILITIES职责描述： 为计划排产、零部件以及包装器具的运输/仓储/配送上线、产线配置、自动化设备导入、成品发运、交通管理等规划或运营业务提供仿真支持 ； 对接各类仿真项目需求，明确项目目标与范围，收集仿真输入数据 ； 针对各业务场景，完成仿真建模与调试，通过仿真试验与分析，支持方案的验证与优化 ； 梳理各场景各项目通用性强的仿真业务单元，开发并维护相应仿真模块库，复用模块提升仿真项目实施效率 ； 仿真培训与推广 ； 研究仿真、算法、数字孪生、虚拟调试、AI等技术的融合创新 ； 遵守公司规章制度，严格按照作业指导书工作，积极查找安全隐患，及时汇报安全隐患和事故，提出安全合理化建议，通过不断改进，创造安全健康的工作环境。 REQUIREMENTS
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校招智能制造
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