康宁
Sr. Software System Engineer
Sr. Software System Engineer
发布于 大约 9 小时前普通员工/个人贡献者
上海市
高级经验
全职员工
仅现场办公
硕士
研究与开发 (研发)
Fine-Tuning
Rag
Scada
大模型
工业Ai
异常检测
时间序列
机器学习
深度学习
AI 估算 · 25k–45k
上海外企高级工程师市场水平，工业AI领域技能稀缺，薪资竞争力较强。
职位详情
关于这个职位
该职位是康宁公司的高级软件系统工程师，主要聚焦工业AI领域，负责从数据治理、建模到前沿大模型应用的全链路技术工作
你将与多团队协作，将算法方案落地到实际制造场景中，推动智能制造和效率提升
最低要求
Education**: Master’s degree or above in Mathematics, Statistics, Automation and Control, Chemical Engineering, Mechanical Engineering, Electrical Engineering, Computer Science, Artificial Intelligence, or other related STEM fields.
Project Experience**: At least 5 years of relevant work experience, with hands-on project experience in industrial scenarios such as equipment condition monitoring, process parameter optimization, feedback control, and product quality monitoring. Experience in lag tracing, correlation/causal analysis, and key factor identification and validation is preferred. Candidates should be capable of independently or jointly leading system design, coding, debugging, and project delivery.
Industrial System Knowledge**: Familiarity with industrial systems and data environments such as DCS, MES, LIMS, SCADA, and OPC, with a solid understanding of industrial business processes and data flows.
Algorithm Expertise**: Strong knowledge of machine learning methods and proficiency in two or three deep learning approaches, such as NN, CNN, RNN, LSTM, GRU, and Encoder/Decoder architectures, with solid algorithm design and modeling capabilities.
Large Model Experience**: Practical experience applying large model technologies in real-world business scenarios. Hands-on experience with at least one or two of the following: RAG, fine-tuning, and LoRA. Experience with large models in industrial scenarios is a strong plus.
Multimodal Data Processing**: Understanding of multimodal industrial data processing workflows, with experience in preprocessing, feature extraction, and modeling of text, image, and audio data.
Programming and Engineering Skills**: Proficiency in Python for algorithm development, modeling, and data processing. Experience with C/C# development is a plus. Familiarity with data structures, algorithm complexity analysis, and software engineering best practices is required.
Engineering Delivery Capability**: Familiarity with Git, unit testing, performance testing, and code review processes. Experience in SQL, API/interface design, and algorithm service deployment, with the ability to take algorithms from prototype to production-grade deployment.
工作职责
Industrial Data Governance and Feature Engineering**: Build standardized data processing pipelines for multi-source industrial data, including process, quality, equipment, and energy-related data. This includes timestamp alignment, data cleaning, anomaly handling, lag analysis, and feature engineering, providing a solid data foundation for modeling and business analysis.
Industrial Modeling and Intelligent Diagnosis**: Design and develop algorithms for industrial scenarios, including time-series forecasting, multivariate process modeling, anomaly detection, fault diagnosis, and soft sensing. Apply quality tracing and causal analysis methods to identify key influencing factors and support process optimization and quality improvement.
Industrial Problem Abstraction and Optimization**: Abstract real-world manufacturing problems into decision variables, constraints, and optimization objectives. Collaborate with domain experts to identify true business constraints, hidden rules, and data boundaries. Based on this, develop solutions using traditional machine learning and deep learning methods, while continuously improving solving efficiency, stability, interpretability, and scalability.
Large Model and AI Application Deployment**: Explore and apply cutting-edge large models (e.g., LLMs, VLMs) and related technologies (e.g., RAG, fine-tuning, LoRA, post-training) to address practical challenges in intelligent manufacturing, such as production efficiency, cost reduction, quality improvement, and energy optimization, and create measurable business value.
Industrial Agent Design and Development**: Define functions, design system architecture, and develop algorithmic solutions for industrial AI agents. Be responsible for feasibility validation, model training, deployment, and practical application, promoting deep integration of AI agents with industrial business workflows.
Technology Research and Innovation**: Track and study the latest developments in large models, AI agents, and industrial AI technologies relevant to manufacturing scenarios. Explore new technologies and methodologies for manufacturing applications and drive continuous innovation.
Cross-functional Collaboration and On-site Support**: Work closely with process, production, equipment, and IT teams to support solution validation, system integration, and large-scale deployment. Travel to factories when necessary for on-site investigation, implementation, and delivery support.
AI 洞察
优缺点分析
优点
- 康宁作为跨国巨头，提供稳定平台和全球化视野
- 聚焦工业AI前沿技术（大模型、AI Agent），技术成长空间大
- 与业务部门紧密合作，能直接看到技术对生产的实际影响
- 需要频繁出差到工厂现场，工作环境可能涉及一定体力
- 工业场景数据复杂且质量参差不齐，算法落地难度高
- 技术栈要求广泛，需要持续学习新工具和方法
- 适合有工业背景、同时对AI前沿技术充满热情的软件工程师，能接受跨部门协作和偶尔的现场支持
缺点 / 挑战
暂无明显挑战项
角色解读
- 向工业AI专家或技术负责人方向深耕
- 可横向拓展至智能制造、工业大数据、数字孪生等领域
- 在康宁内部跨部门或跨国项目积累管理经验
- 构建工业数据治理与特征工程管道，清洗多源数据并提取特征
- 开发时序预测、异常检测、故障诊断等算法模型
- 探索并部署大模型（LLM/VLM）及RAG、微调技术解决制造问题
- 设计工业AI智能体的系统架构并推动落地
- 精通Python和机器学习/深度学习框架，熟悉多种神经网络结构
- 掌握大模型应用技术，如RAG、LoRA、Fine-tuning
- 了解工业系统（DCS、MES、SCADA等）及数据流
- 具备工程化能力，从原型到生产部署（Git、SQL、API）
申请策略
- 在面试中准备一个完整的工业AI项目故事，从问题抽象到上线全流程
- 了解康宁的业务方向（显示玻璃、光纤、环境技术等），展现行业热情
- 突出工业项目经历，特别是设备监控、质量优化等实际案例
- 展示大模型应用成果，如用RAG或微调解决具体问题
- 强调工程化能力：部署过哪些算法服务，使用过哪些CI工具
- 丰富大模型实战经验，建议做1-2个端到端的微调或RAG项目
- 补充工业协议（OPC、SCADA）和数据处理的基础知识
面试指南
- 用STAR法则：情境（Situation）、任务（Task）、行动（Action）、结果（Result），量化工况改善指标
- 对于技术问题，先阐述原理再结合经验，体现深度和广度
- 请描述一个你从零开始构建工业异常检测系统的项目
- 大模型（LLM）在工业场景中有哪些实际应用案例？遇到过什么挑战？
- 如何处理工业数据中的缺失值和噪声？
- 你对AI Agent架构的理解是什么？在设计工业Agent时需考虑哪些因素？
- 复习时间序列、异常检测、因果推断等常用工业算法
- 准备一个你主导的算法工程化项目，涵盖从数据到部署
职位点评
71
综合评分
技术前沿、薪资可观，但工作地点固定且需出差，适合发展动机强的求职者。
从薪资福利、成长空间、工作节奏和岗位方向综合评估，方便横向比较。
更适合这类人
适合追求技术成长、愿意接受出差挑战的资深工程师。
表现最好
成长发展
相对薄弱
薪资福利
薪资福利60
成长发展90
工作生活60
使命价值75
薪资福利
60中等
薪资未在JD中披露，但考虑外企和高级岗位，市场水平中等偏上；福利未提及，补偿性满足一般。
薪资信号未披露（AI估算：25K-45K/月）
成长发展
90较高
职位涉及大模型、AI Agent等前沿技术，并鼓励创新，成长空间大。
技术前沿前沿/新兴技术
技术栈RAG、Fine-tuning、LoRA、LLM、VLM、深度学习
业务类型ambiguous
工作生活
60中等
工作地在上海，但需要出差至工厂，未提及弹性办公，生活便利性一般。
工作模式仅现场办公
办公地点未明确
加班情况未提及（无法判断）
使命价值
75中等
智能制造促进工业效率与节能，社会价值较高，且公司重视创新。
行业发展稳定成熟行业
社会影响正向社会影响力较高
创新程度积极采用新技术
康宁 的其他在招职位
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