工程师 - 计算机视觉研发 / 应用AI

该职位来源于猎聘 Main Responsibilities: 1.

Computer

Vision

• Apply knowledge of hardware, software, and cutting-edge technology (specifically Machine Learning, Computer Vision, and other AI technologies) to drive operational efficiencies and world-class guest experiences
• Design, develop, and deploy computer vision and AI-driven solutions (e.g., video analytics, object detection, facial recognition, behavioral insights) to improve operational efficiency and guest experience
• Work with video/image data pipelines, including data collection, labeling, training, and model evaluation
• Conduct proof-of-concept studies as appropriate (e.g. technical feasibility study) includes performing analysis such as time motion, 5 Whys, Value Stream Mapping, etc.
• Develop operational metrics that measure the need for, and the impact of, innovation.
• Facilitate / lead stakeholders across various departments to deliver results, promote a culture of innovation and continuous improvement.
  • AI Engineer

• Develop machine learning, analytics, and AI solutions to solve business problems, and assist with monitoring, troubleshooting, and continuous improvement。
• Partner with Data Scientists to build and maintain data pipeline, train models, evaluate results, and support experimentation.
• Develop dashboards, applications, APIs, and user interfaces that make AI solutions accessible to business users.
• Analyze data to identify trends, opportunities, and insights that support business decisions. Requirements/Qualifications:
• Experience in software engineering / machine learning / applied AI roles / Data Science / Machine Learning / Software Engineering or related field.
• Strong hands-on experience in computer vision and deep learning, including:

Image/video processing

• Object detection, tracking, classification
• Facial recognition or biometric systems (preferred)
• Familiarity with machine learning concepts and common AI frameworks. (Preferred experience with Large Language Models (LLMs), Computer Vision, or predictive analytics).
• Experience working with data pipelines, APIs, or cloud-based applications (Azure, AWS, or other cloud platforms).