As a member of the Laser Application Center R&D team, you will be responsible for designing and implementing deep learning and image-processing algorithms based on industrial laser processing inspection and process-monitoring data, and for building and maintaining AI platforms and data pipelines tailored to laser processes. The goal is to convert complex optical/process images and sensor data into reusable models and services that support process optimization and intelligent inspection for laser welding, cutting, surface treatment, and other laser applications.
This role requires strong ownership, system-level thinking, and the ability to work independently in complex industrial environments.
作为激光应用中心 R&D 团队的一员，您将负责基于工业激光加工检测与过程监测数据，设计并实现深度学习与图像处理算法，搭建并维护面向激光工艺的 AI 平台与数据管道。目标是把复杂的光学/工艺图像与传感器数据转化为可复用的模型与服务，支撑激光焊接/切割/表面处理等工艺优化与智能检测需求
该岗位需要较强的责任意识与系统思维，能够在复杂工业环境中独立推进研发任务。

1. Design, develop and optimize PyTorch-based deep learning models for industrial imaging tasks such as defect detection, object segmentation, classification and temporal analysis, ensuring model robustness and real-time performance in industrial environments.
设计、开发并优化基于 PyTorch 的深度学习模型，用于缺陷检测、目标分割、分类与时序分析等工业图像任务，保证模型在工业场景下的鲁棒性与实时性；
2. Develop preprocessing and augmentation algorithms for camera/sensor images and signals, and engineer image-processing modules for integration into the platform.
负责相机/传感器图像与信号的预处理与增强算法，并将图像处理模块工程化以便集成到平台
3. Build and maintain pipelines for industrial laser data collection, annotation, storage and versioning; design data standards and labeling schemes to drive data quality control and traceability.
搭建并维护工业激光数据的采集、标注、存储与版本管理流程，设计数据标准与标签体系，推动数据质量控制与可追溯性；
4. Support on-site validation and pilot deployment of AI solutions together with application engineers, ensuring performance under real production conditions.
与应用工程师协作，支持 AI 方案在真实产线和客户现场的验证与试点部署，确保工业条件下的稳定性与可靠性；
5. Prepare technical documentation, test reports and user manuals, and provide internal training and support for model/platform usage.
编写技术文档、测试报告与使用手册，并对内部用户进行模型/平台使用培训与支持；
6. Track academic and industry developments, evaluate and introduce appropriate algorithms, tools and best practices to continually improve the platform.
跟踪前沿学术与工业进展，评估并引入适合的算法、工具与最佳实践，不断提升平台能力。

Proficient in PyTorch, with hands-on experience implementing models from scratch, training and hyperparameter tuning, model compression/acceleration, and inference deployment.
熟练掌握 PyTorch，具备从零实现模型、训练调参、模型压缩/加速与推理部署的实践经验；



Familiar with industrial cameras, image-acquisition workflows, and common image formats; knowledgeable in camera calibration, distortion correction, and geometric transformations.
熟悉工业相机、图像采集流程与常见图像格式，了解相机标定、畸变校正与几何变换

Familiar with development in Python (proficient with numpy, OpenCV, PyTorch, etc.) and familiar with common data-processing and visualization tools.
能用 Python 进行日常开发（熟练使用 numpy, opencv, torch 等库），熟悉常用数据处理与可视化工具

Practical experience deploying algorithms or platforms in industrial laser welding/cutting/cleaning/surface-treatment projects is a prefered.
有在工业激光焊接/切割/清洗/表面处理相关项目中的实际算法或平台落地经验者优先

Experience with deploying applications on AWS, Azure, or other major cloud platforms is a plus
有在 AWS、Azure 或其他主要云平台上部署应用的经验者优先

Experience with IP application
有专利申请经验者优先