Editorial summary

The company, Nanyang Polytechnic, seeks a dynamic Lecturer/Senior Lecturer in Edge AI Product Development to contribute to the School of Engineering. The role involves developing, optimizing, and deploying intelligent edge solutions using machine learning on edge devices, single-board computers, and microcontrollers, as well as teaching and mentoring students across Diploma and Adult Learning programmes. Key requirements include expertise in edge AI, practical development experience, and involvement in curriculum development and modern pedagogy. Compensation details are not provided in the description.

This summary is AI-generated and may contain inaccuracies. Please refer to the full job description below.

Job description

[This job https://singapore.job-q.com/jobs/detail/lecturer-snr-lecturer-edge-ai-solution-sch-of-engineering-42850 first appeared in Job-Q.com on 13 Nov 2026]

[What the role is]

Nanyang Polytechnic is seeking a dynamic and experienced Lecturer/Senior Lecturer in Edge AI Product Development to contribute to our School of Engineering. In this role, you will develop, optimise and deploy intelligent system solutions directly on the edge devices using machine learning algorithms and single board computing devices and microcontrollers.
This role also involves teaching and mentoring students across both PET (Diploma) and CET (Adult Learning) programmes using modern pedagogical approaches, curriculum development in emerging technology domains, industry engagement, applied research, and academic administration.

[What you will be working on]

Instructional Delivery & Facilitation

• Deliver lectures, tutorials and laboratory sessions across full-time Diploma programs (PET) and adult workforce upskilling modules (CET).
• Adopt diversified pedagogical frameworks, including project-based learning, flipped classrooms, and blended e-learning spaces.
• Supervise and mentor students during final-year projects, multi-disciplinary capstone assignments, and professional internship placements, particularly in Edge AI, IoT systems, and distributed architectures.

Curriculum Design & Development

• Design, review, and revitalize instructional resources, module structures, and assessment rubrics aligned to emerging industry practices in Edge AI and distributed systems.
• Develop hands-on lab modules involving embedded AI deployment, IoT telemetry pipelines, and hardware acceleration platforms.

Industry Engagement & Applied Research

• Establish partnerships with industry players in Edge AI, IoT, and smart infrastructure.
• Contribute to applied R&D and industry partnership in areas such as edge AI, digital twin systems can smart connected systems.

Student Development & Administrative Duties

• Mentor students in technical and career development.
• Perform academic administration duties including assessment tracking and quality assurance.

#LI-Hybrid

[What we are looking for]

• Education: Degree or Postgraduate qualification in Electronic Engineering, Computer Science or related fields.
• Industry Experience: Minimum 3–5 years in software development, AI application development, or product engineering.

(Candidate should demonstrate strong proficiency in the following areas) 

Computer Vision and AI Framework

• 1–2 years of hands-on experience in machine learning, deep learning, or computer vision application development.
• Familiarity with frameworks such as PyTorch, TensorFlow, YOLO, or OpenCV for object detection, image classification, segmentation, or object tracking.
• Experience with image annotation tools and preparing datasets for AI model training.
• Basic knowledge of common dataset formats such as YOLO and COCO, including dataset conversion and augmentation.
• Familiarity with Python and basic AI model training, testing, and performance evaluation.
• Some practical exposure to NVIDIA Jetson or similar edge AI platforms, including the use of JetPack SDK, CUDA, TensorRT, or equivalent tools for deploying AI models.
• Basic experience in microcontroller programming using MicroPython or C/C++ for IoT applications, including interfacing with sensors or communication devices.
• Exposure to LLMs, Vision-Language Models (VLMs), or agentic AI frameworks would be an advantage.
• Exposure to deploying AI models on edge devices or GPU-based systems would be an added advantage.

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