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nahc.io

Research Scientist

Hong Kong

Location
Hong Kong
Work
On-site
Employment
Full-time
Level
Not stated
Function
Science & research
Pay
Not stated
Posted
5 weeks ago

About the role

Overview

Our client is a fast-growing, profitable regional AI tech company headquartered in Singapore, with a strong presence across APAC including Malaysia, Taiwan, Thailand, and Japan. They deploy industrial AI and deep tech solutions to optimize operations and drive efficiency for major enterprise and listed clients in heavy industries such as manufacturing and oil & gas. As part of their regional expansion, they are establishing the core R&D team in HK to tackle complex technical challenges at the intersection of AI, hardware, and industrial systems.

What You Will Do

  • Design, develop, and deploy industrial AI algorithms to optimize manufacturing processes, shop floor operations, and equipment performance.
  • Collaborate closely with cross-functional hardware and technical solutions teams to integrate AI models with sensors, processors, and physical devices.
  • Conduct hands-on testing, validation, and prototyping for enterprise solutions.
  • Work directly on hard technical problems to improve operational efficiency for large-scale enterprise deployments.
  • Train and mentor deployment engineers (including regional team members) on implementation best practices.

What You Will Need

  • PhD or Master’s degree in Computer Science, Mechanical Engineering, Automation, or a related technical discipline.
  • Hands-on experience developing and deploying AI/ML models or advanced algorithms for hardware systems and sensor data.
  • Background in industrial controls, embedded processors, PLCs, or manufacturing automation is preferred.
  • Practical problem-solving mindset with a passion for bridging software and physical hardware applications.
  • Good command of English and Chinese (Cantonese or Mandarin) to collaborate effectively with regional teams and clients.

Source

Posted by nahc.io on their own hiring page, jobs.lever.co. OptiTalent shows it as published, read 07 Nov 2026 00:19 UTC; the original is the reference.

View the original ad ↗

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