理想汽车电驱软件AI开发高级工程师
社招全职3年以上汽车研发地点:上海状态:招聘
任职要求
教育背景: 1.硕士及以上学历,计算机科学、人工智能、车辆工程等相关专业; 工作经验: 1.具备3年以上AI算法、应用等关键技术领域的研发经验; 2.有AI项目(嵌入式芯片集成、AI智能体等)落地经验,具备全流程开发能力; 专业技能: 1.掌握Python、C++、C等编程语言,掌握TensorFlow、PyTorch等深度学习框架; 2.熟练掌握数据分析、机器学习相关算法(LR、GBDT、LSTM、Transformer等),并具有直接开发应用经验; 3.熟练掌握prompt Engineering相关方案(CoT、few-shot CoT、ToT、ReAct、ART)等; 4.熟悉常见的大语言模型结构(DeepSeek、GPT、LLaMA、BERT、ChatGLM)以及模型结构优化方案; 5.对动驱软件有深入了解,能够将AI技术与业务需求紧密结合; 6.在国际顶级会议(如ICML、NeurIPS、ICLR、CVPR等)或期刊(如JMLR、TPAMI等)上发表过高水平学术论文,具有一定的学术影响力着优先; 7.参与过大型AI项目的研发和落地,具有丰富的实战能力优先; 能力素质: 1.具备良好的沟通能力、团队协作精神; 2.具备创新思维、解决复杂技术问题的能力。
工作职责
1. 负责AI算法在电机控制、电源控制或诊断预警等领域的应用,以及在嵌入式芯片部署落地; 2. 负责基于动驱软件业务场景大语言模型应用的数据探索、Prompt Engineering、模型微调等工作,软件AI智能体落地; 3. 建立软件数据管理体系,保障数据的准确性、完整性和安全性; 4. 持续跟进业内技术方向并与团队业务方向结合进行技术方向规划指定。
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/
智能体+
https://learn.microsoft.com/en-us/shows/ai-agents-for-beginners/
In this 10-lesson course we take you from concept to code while covering the fundamentals of building AI agents.
https://www.ibm.com/think/ai-agents
Your one-stop resource for gaining in-depth knowledge and hands-on applications of AI agents.
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.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
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.
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.
深度学习+
https://d2l.ai/
Interactive deep learning book with code, math, and discussions.
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
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.
GBDT+
https://developers.google.com/machine-learning/decision-forests/intro-to-gbdt
Like bagging and boosting, gradient boosting is a methodology applied on top of another machine learning algorithm.
https://scikit-learn.org/stable/modules/ensemble.html
Ensemble methods combine the predictions of several base estimators built with a given learning algorithm in order to improve generalizability / robustness over a single estimator.
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.
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.
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!
React+
[英文] Quick Start - React
https://react.dev/learn
This page will give you an introduction to 80% of the React concepts that you will use on a daily basis.
https://www.youtube.com/watch?v=SqcY0GlETPk
Master React 18 with TypeScript! ⚛️ Build amazing front-end apps with this beginner-friendly tutorial.
https://www.youtube.com/watch?v=x4rFhThSX04
Learn modern React basics in the most interactive, hands-on way possible in the full course for beginners.
GPT+
https://www.youtube.com/watch?v=kCc8FmEb1nY
We build a Generatively Pretrained Transformer (GPT), following the paper "Attention is All You Need" and OpenAI's GPT-2 / GPT-3.
Llama+
https://github.com/LlamaFamily/Llama-Chinese
Llama中文社区,实时汇总最新Llama学习资料,构建最好的中文Llama大模型开源生态,完全开源可商用。
https://www.llama.com/docs/overview/
This guide provides information and resources to help you set up Llama including how to access the model, hosting, how-to and integration guides.
BERT+
https://www.youtube.com/watch?v=xI0HHN5XKDo
Understand the BERT Transformer in and out.
ChatGLM+
https://www.youtube.com/watch?v=EXUX0MjBzI0
In this step-by-step tutorial, you'll learn how to use ChatGLM, one of the most powerful and completely free AI video generators available today.
https://www.youtube.com/watch?v=fGpXj4bl5LI
Exploring the concept of a GLM (General Language Model) and working with ChatGLM6B.
ICML+
https://icml.cc/
NeurIPS+
https://neurips.cc/
ICLR+
https://iclr.cc/
CVPR+
https://cvpr.thecvf.com/
相关职位
社招汽车研发
1.电驱动总成匹配应用相关功能和性能需求收集、分析与实施方案制定; 2.电驱系统(电机、逆变器、减速器等)的台架标定与性能优化,包括扭矩控制、效率标定、热管理策略、NVH调校等,确保系统高效、可靠运行; 3.主导电驱系统与整车控制单元(VCU)的协同标定,优化动力分配策略,提升驾驶性、经济性及动力性表现; 4.参与下一代电驱技术的标定开发,推动电驱系统的高功率密度、轻量化及低能耗目标实现; 5.支持智能化线控底盘技术(线控制动、线控转向)的集成标定,确保与电驱系统的协同控制; 6.策划并执行电驱系统台架及整车标定试验(含高温、高原、高寒“三高”环境测试),分析数据并解决标定和测试过程中的技术问题; 7.对量产及售后问题提供技术支持,主导故障根因分析与改进方案制定; 8.制定电驱系统标定流程、技术规范及工具链建设,推动标定效率提升; 9.指导初级工程师,组织技术培训,引入AI驱动的标定工具(如端到端模型优化、大语言模型辅助决策); 10.标定数据评审、版本管理、需求的闭环认可和数据发布; 11.面向横向产品开发团队的标定数据与测试支持; 12.内部项目的横向组织和协调,制定方案持续推动性能优化,达成产品竞争力目标。
社招5年以上技术类-开发
1、带领硬件团队,主导具身智能机器人的系统架构设计及核心器件选型, 全面负责机器人本体硬件平台(结构、电驱、传感器系统)的顶层设计、技术路线制定与关键器件评估选型; 2、与内部产研测等团队和外部供应商配合,推动形成完整的硬件产品实现规模量产; 3、引领技术前沿与构建影响力,持续跟踪并研判行业前沿技术方案(硬件、控制、感知与AI融合),主导具身智能软硬件协同的核心技术攻关。
更新于 2025-09-25