工业人工智能培训生

Explore and apply emerging AI technologies to solve real industrial challenges, and transform validated AI use cases into scalable products and solutions that create measurable customer value.

The Industrial AI Trainee will be developed as a cross-functional talent combining AI application capabilities, industrial understanding and product thinking. The role will work with customers, sales, product and R&D teams to explore industrial AI opportunities, analyze real-world manufacturing data, develop AI prototypes and Proofs of Concept, and gradually take ownership of Industrial AI use cases and product features.

岗位使命

探索和应用前沿人工智能技术，解决真实的工业问题，并将经过验证的 AI 应用场景转化为可扩展、可复制的产品和解决方案，为客户创造可衡量的价值。

工业人工智能培训生将被培养为兼具 AI 应用能力、工业场景理解和产品思维的复合型人才。该岗位将与客户、销售、产品及研发团队协作，识别工业 AI 机会，分析真实制造数据，开发 AI 原型及概念验证，并逐步独立负责工业 AI 应用场景和产品功能。

Major Responsibilities

Duties and responsibilities include, but are not limited to:

Explore Industrial AI opportunities in tightening, assembly and other smart manufacturing scenarios, and translate customer pain points into clearly defined AI use cases.

Participate in customer visits and factory-site investigations to understand manufacturing processes, quality challenges, operational workflows and actual user needs.

Collect, understand and analyze industrial data, including time-series data, tightening curves, process data, quality data and equipment-related data.

Independently develop small-scale AI prototypes and Proofs of Concept, covering data preparation, data cleaning, feature engineering, model development, model evaluation and result visualization.

Evaluate the technical feasibility, model performance, business relevance and customer value of Industrial AI use cases.

Participate in the complete Industrial AI development cycle, including industrial problem definition, data analysis, AI approach selection, PoC development, product feature definition and customer validation.

Work closely with Product Managers and R&D teams to convert validated AI concepts into product requirements, user stories, workflows and scalable product features.

Support product testing, pilot projects and on-site validation, and continuously improve AI solutions based on real-world results and customer feedback.

Support AI demonstrations, technical presentations, customer workshops and solution discussions, and gradually develop the ability to communicate Industrial AI value directly to customers.

Collaborate with sales and project teams to identify customers’ potential AI needs and support relevant technical and solution activities.

Work with global Product Company and R&D teams on AI technologies, product concepts, technical feasibility and new customer requirements.

Document project findings, technical approaches, experimental results and product requirements in a clear and structured manner.

Continuously develop knowledge of industrial assembly, tightening technologies, smart manufacturing, industrial software and AI product development.

任务职责包括但不限于：

探索拧紧、装配及其他智能制造场景中的工业 AI 机会，并将客户痛点转化为定义清晰的 AI 应用场景。

参与客户拜访及工厂现场调研，了解制造过程、质量问题、运营工作流和用户实际需求。

收集、理解和分析工业数据，包括时序数据、拧紧曲线、工艺数据、质量数据及设备相关数据。

独立开发小型 AI 原型和概念验证，涵盖数据准备、数据清洗、特征工程、模型开发、模型评估及结果可视化。

评估工业 AI 应用场景的技术可行性、模型表现、业务相关性和客户价值。

参与完整的工业 AI 开发闭环，包括工业问题定义、数据分析、AI 方法选择、PoC 开发、产品功能定义和客户验证。

与产品经理及研发团队紧密合作，将经过验证的 AI 概念转化为产品需求、用户故事、工作流程和可规模化的产品功能。

支持产品测试、试点项目及客户现场验证，并根据实际运行结果和客户反馈持续改进 AI 解决方案。

支持 AI 演示、技术介绍、客户研讨会及解决方案交流，并逐步培养直接向客户阐述工业 AI 价值的能力。

与销售和项目团队合作，识别客户潜在的 AI 需求，并支持相关技术及解决方案活动。

与全球产品公司及研发团队就 AI 技术、产品概念、技术可行性及新的客户需求开展合作。

以清晰、结构化的方式记录项目发现、技术方案、实验结果和产品需求。

持续学习工业装配、拧紧技术、智能制造、工业软件和 AI 产品开发相关知识。

Education & Experience

Bachelor’s or master’s degree in Artificial Intelligence, Computer Science, Software Engineering, Data Science, Automation, Electrical Engineering, Mechatronics or another relevant engineering discipline.

Practical experience from at least one AI( machine learning, deep learning, data science or generative AI) project is expected.

Experience in independently completing a small AI project covering data processing, model training, model evaluation and result visualization is preferred.

Experience with Industrial AI( smart manufacturing, robotics, machine vision, predictive maintenance, quality analytics or industrial data analysis) is an advantage, but is not mandatory.

Knowledge or experience related to industrial assembly, tightening, manufacturing quality or industrial software is an advantage, but is not mandatory.

教育和经验要求：

人工智能、计算机科学、软件工程、数据科学、自动化、电气工程、机械电子或其他相关工程专业，本科或硕士学历。

应具备至少一个人工智能(机器学习、深度学习、数据科学或生成式 AI)项目的实际经验。

有独立完成小型 AI项目的经验者优先，包括数据处理、模型训练、模型评估和结果可视化。

具备工业 AI(智能制造、机器人、机器视觉、预测性维护、质量分析或工业数据相关)项目经验者优先，但不作为硬性要求。

具备工业装配、拧紧、制造质量或工业软件相关知识或项目经验者优先，但不作为硬性要求。

Knowledge & Skills

Basic product thinking and the ability to connect customer problems, user requirements, technical feasibility and product value.

Strong analytical skills and the ability to use data and experiments to validate assumptions.

Ability to communicate technical concepts in a clear, structured and business-relevant manner.

Good English communication skills, both written and spoken, with the ability to read technical materials, prepare presentations and reports, and communicate with global teams.

Good presentation and demonstration skills, or a strong willingness to develop such capabilities.

Basic skills in Microsoft Office applications, especially PowerPoint and Excel

Good understanding of fundamental machine learning concepts, including supervised learning, unsupervised learning, regression, classification, clustering and model evaluation.

Practical Python programming skills and experience using relevant data analysis and AI libraries,

Experience with at least one deep learning framework, such as PyTorch or TensorFlow, is preferred.

Ability to independently complete a basic AI workflow, including data loading, data cleaning, exploratory data analysis, feature engineering, model training, validation, evaluation and visualization.

Basic understanding of common machine learning concepts, with hands-on experience in one or more AI methods such as Random Forest, XGBoost, neural networks, CNNs, Transformers, time-series analysis or anomaly detection

Basic understanding of generative AI, large language models, prompt engineering, retrieval-augmented generation or AI Agent concepts.

知识技能：

具备基本的产品思维，能够将客户问题、用户需求、技术可行性和产品价值联系起来。

具备良好的分析能力，能够通过数据和实验验证假设。

能够以清晰、结构化并与业务场景相关的方式解释技术概念。

具备良好的英语口语及书面沟通能力，能够阅读技术资料、制作演示文稿和报告，并与全球团队沟通。

具备良好的演讲和演示能力，或有强烈意愿发展相关能力。

具备基本的 Microsoft Office 应用能力，尤其是 PowerPoint 和 Excel。-

良好理解机器学习基础概念，包括监督学习、无监督学习、回归、分类、聚类及模型评估。

具备 Python 编程实践能力，并使用过相关数据分析和 AI 工具库。

使用过至少一种深度学习框架者优先，例如 PyTorch 或 TensorFlow。

能够独立完成基本的 AI 工作流程，包括数据读取、数据清洗、探索性数据分析、特征工程、模型训练、验证、评估和可视化。

理解常见机器学习基本概念，并至少对一种或多种 AI 方法具备实际项目经验，例如随机森林、XGBoost、神经网络、CNN、Transformer、时序分析或异常检测等。

基本理解生成式 AI、大语言模型、提示词工程、检索增强生成或 AI Agent 等概念。

Personality & Behavior Traits

Self-learning and curiosity: Demonstrates strong interest in emerging AI technologies and continuously learns new concepts, tools and application methods.

Problem solving: Able to break down ambiguous industrial challenges into structured and actionable problems.

Hands-on and result oriented: Willing to work directly with data, write code, conduct experiments and develop prototypes instead of remaining at the conceptual level.

Product thinking: Focuses not only on model performance, but also on customer needs, usability, scalability and business value.

Communication: Able to communicate effectively with customers, sales, product, project and R&D teams from different technical and cultural backgrounds.

Proactive and willing to take ownership.

Open-minded and willing to work across technology, product and business functions.

Comfortable working with uncertainty and learning through experimentation.

Team-oriented and willing to give and receive constructive feedback.

Customer-focused and willing to understand real manufacturing environments and operational challenges.

性格和行为特质：

自主学习与好奇心： 对前沿 AI 技术保持浓厚兴趣，并持续学习新的概念、工具和应用方法。

问题解决能力： 能够将模糊、复杂的工业挑战拆解为结构化、可执行的问题。

动手能力与结果导向： 愿意直接处理数据、编写代码、开展实验和开发原型，而不仅仅停留在概念讨论层面。

产品思维： 不仅关注模型表现，同时关注客户需求、易用性、可扩展性和业务价值。

沟通能力： 能够与来自不同技术及文化背景的客户、销售、产品、项目和研发团队有效沟通。

积极主动，愿意逐步承担责任。

思维开放，愿意跨技术、产品和业务职能开展工作。

能够适应不确定性，并通过实验和实践持续学习。

具有团队合作意识，愿意给予并接受建设性反馈。

以客户为导向，愿意深入了解真实制造环境和运营挑战。