AI Engineer

Company Description

MMSTAR Technologies Limited, based in the HKSTP, is a technology start-up company jointly launched with professors from the Hong Kong University of Science and Technology and former senior executives from listed companies, specializing in smart aquaculture AIoT and aquatic ecosystem monitoring technologies. The company develops automated AIoT systems to assist aquaculture operators, researchers, and environmental managers in monitoring water quality and ecosystem health with minimal manual intervention. Our flagship platform, DataStar, combines water handling, imaging modules, machine learning, and remote control to facilitate scalable monitoring in natural and aquaculture environments.

About the Role – Smart Aquaculture & Computer Vision

We are developing an AIoT-based aquaculture monitoring device that combines sensors, cameras, and edge/cloud intelligence to support data-driven aquaculture operations.We are seeking an AI Engineer to develop and deploy computer vision and data analytics models for real-world aquaculture environments, with a focus on robustness, scalability, and system integration.

Key Responsibilities

• Develop and train computer vision models for microorganism monitoring, water condition assessment, and object detection

• Design and optimise end-to-end AI pipelines from data collection to inference (edge and/or cloud)

• Process and analyse image, sensor, and time-series data

• Improve model robustness under challenging conditions (low light, turbidity, noise, biofouling)

• Deploy and maintain models on edge devices and cloud platforms

• Collaborate closely with hardware and embedded teams to align AI performance with system constraints

• Evaluate model performance and iteratively improve accuracy and efficiency

Requirements

• Bachelor’s Degree or above in Computer Science, AI, Data Science, or related discipline (or currently pursuing one)

• Year 3 / Year 4 undergraduate or postgraduate students are also welcome to apply as interns.

• Solid foundation in machine learning and computer vision

• Experience with Python and common ML frameworks (e.g. PyTorch, TensorFlow)

• Familiarity with image processing and data preprocessing workflows

• Ability to work with real-world datasets

Preferred Qualifications

• Experience with edge AI, model optimisation, or deployment on embedded systems

• Familiarity with cloud platforms (e.g. AWS) and data pipelines

• Experience with time-series or multimodal data (image + sensor data)