Image Recognition and Machine Learning Application Development Engineer (1-year traineeship programme)

This project is with Nanolumi, a startup supported by SGInnovate under the PowerX Programme.

PowerX is a 12-months traineeship programme designed to enable and create Deep Tech career opportunities for working professionals, combining bespoke training and industry experience.

ABOUT THE COMPANY:

Nanolumi is an advanced materials company that delivers breakthrough luminescent solutions for Product Authentication (Reyal) and Bio-imaging. The company do this by harnessing cutting-edge nano / molecular chemistries and optical system to create, detect, and manipulate light.

The team’s expertise lies in identifying & optimizing technical synergies, building comprehensive solutions through integrations & partnerships, and fostering supply chain collaborations to accelerate new technology adoption.

ABOUT THE PROJECT:

Reyal is a new, cloud-based, multi-layer solution with the technology and tools for companies to connect every physical product with a unique digital identity for secure product authentication & counterfeit protection. The Reyal solution is a combination of the company's multi-disciplinary knowledge in advanced material science, photonics and electronics, as well as marketing.

With the solution expansion and product development, the authentication process and system need to be upgraded continuously. One focus area is to develop AI-based image recognition and analysis detection mechanism in the smartphone platform system for anti-counterfeit authorization.

ABOUT THE RESPONSIBILITIES:

The job scope of the Image Recognition and Machine Learning Application Development Engineer typically involves a range of tasks related to developing, implementing, and optimizing algorithms and systems for analysing and interpreting visual image data using machine learning techniques for anti-counterfeit authorization in the smartphone platform. Day-to-day operation support and continuous improvement in the image recognition algorithm are also part of the job portfolio.

1. Algorithm and model development for image recognition and analysis.
2. Large datasets of images collection and pre-processing for training machine learning models.
3. Model training and evaluation and deep learning frameworks establishment.
4. Model optimization and deployment to production environments such as cloud platforms, edge devices, or embedded systems.
5. Feature engineering from images to improve model performance and interpretability.
6. Understanding the specific anti-counterfeit application area where image recognition is being applied and tailoring algorithms and models accordingly.
7. Model evaluation and validation by using appropriate metrics and validation techniques to ensure the accuracy, robustness, and reliability of the image recognition system.
8. Model deployment and integration into production environments. Ensure seamless integration with existing infrastructure and maintain compatibility with different platforms and frameworks.
9. Monitor the performance of deployed models and systems, and identifying and addressing challenges and limitations in existing algorithms or systems. Troubleshooting any issues or anomalies that arise, and debugging issues related to data quality, model performance, or system integration. Perform regular maintenance and updates to keep the system up-to-date and functioning optimally.

ABOUT THE CANDIDATE:

The candidates should be with a combination of technical skills, domain knowledge, and relevant working experience, plus a passion for learning for a successful image recognition and machine learning application software.

1. Education and Working Experience: A bachelor's or higher in computer science or other related fields with strong computer science knowledge and experience. One or more years of working experience, preferably with relevant project experience in machine learning and computer vision.
2. Strong Programming Skills: Proficiency in programming languages commonly used in machine learning and computer vision, such as Python, Java, C++, and MATLAB.
3. Machine Learning and Deep Learning: In-depth understanding of machine learning concepts, algorithms, and techniques, including supervised and unsupervised learning, deep learning architectures, optimisation algorithms, and model evaluation methods. Ability to implement, train, and evaluate machine learning models using these tools
4. Computer Vision: Familiarity with computer vision principles, techniques, and libraries. Experience with tasks such as object detection, image classification, segmentation, and feature extraction.
5. Data Handling and Processing: Proficiency in data manipulation, pre-processing, and feature engineering techniques. Experience with tools and libraries for data handling and manipulation, such as Pandas, NumPy, and MATLAB.
6. Software Development Skills: Strong software engineering fundamentals, including proficiency in software design, data structures, algorithms, version control systems, and software testing.
7. Creative Problem-Solving Abilities
8. Good Collaboration and Communication Skills: Abilities to work in cross-functional teams and communicate technical concepts and ideas clearly to both technical and non-technical stakeholders.
9. Domain Knowledge: Not pre-requirement for the company’s specific application domain, but some familiarity with the company’s business background definitely is beneficial.

This project is with Nanolumi, a startup supported by SGInnovate under the PowerX Programme.

PowerX is a 12-months traineeship programme designed to enable and create Deep Tech career opportunities for working professionals, combining bespoke training and industry experience.

ABOUT THE COMPANY:

Nanolumi is an advanced materials company that delivers breakthrough luminescent solutions for Product Authentication (Reyal™) and Bio-imaging. The company do this by harnessing cutting-edge nano / molecular chemistries and optical system to create, detect, and manipulate light.

The team’s expertise lies in identifying & optimizing technical synergies, building comprehensive solutions through integrations & partnerships, and fostering supply chain collaborations to accelerate new technology adoption.

ABOUT THE PROJECT:

Reyal™ is a new, cloud-based, multi-layer solution with the technology and tools for companies to connect every physical product with a unique digital identity for secure product authentication & counterfeit protection. The Reyal™ solution is a combination of the company's multi-disciplinary knowledge in advanced material science, photonics and electronics, as well as marketing.

With the solution expansion and product development, the authentication process and system need to be upgraded continuously. One focus area is to develop AI-based image recognition and analysis detection mechanism in the smartphone platform system for anti-counterfeit authorization.

ABOUT THE RESPONSIBILITIES:

The job scope of the Image Recognition and Machine Learning Application Development Engineer typically involves a range of tasks related to developing, implementing, and optimizing algorithms and systems for analysing and interpreting visual image data using machine learning techniques for anti-counterfeit authorization in the smartphone platform. Day-to-day operation support and continuous improvement in the image recognition algorithm are also part of the job portfolio.

1. Algorithm and model development for image recognition and analysis.
2. Large datasets of images collection and pre-processing for training machine learning models.
3. Model training and evaluation and deep learning frameworks establishment.
4. Model optimization and deployment to production environments such as cloud platforms, edge devices, or embedded systems.
5. Feature engineering from images to improve model performance and interpretability.
6. Understanding the specific anti-counterfeit application area where image recognition is being applied and tailoring algorithms and models accordingly.
7. Model evaluation and validation by using appropriate metrics and validation techniques to ensure the accuracy, robustness, and reliability of the image recognition system.
8. Model deployment and integration into production environments. Ensure seamless integration with existing infrastructure and maintain compatibility with different platforms and frameworks.
9. Monitor the performance of deployed models and systems, and identifying and addressing challenges and limitations in existing algorithms or systems. Troubleshooting any issues or anomalies that arise, and debugging issues related to data quality, model performance, or system integration. Perform regular maintenance and updates to keep the system up-to-date and functioning optimally.

ABOUT THE CANDIDATE:

The candidates should be with a combination of technical skills, domain knowledge, and relevant working experience, plus a passion for learning for a successful image recognition and machine learning application software.

1. Education and Working Experience: A bachelor's or higher in computer science or other related fields with strong computer science knowledge and experience. One or more years of working experience, preferably with relevant project experience in machine learning and computer vision.
2. Strong Programming Skills: Proficiency in programming languages commonly used in machine learning and computer vision, such as Python, Java, C++, and MATLAB.
3. Machine Learning and Deep Learning: In-depth understanding of machine learning concepts, algorithms, and techniques, including supervised and unsupervised learning, deep learning architectures, optimisation algorithms, and model evaluation methods. Ability to implement, train, and evaluate machine learning models using these tools
4. Computer Vision: Familiarity with computer vision principles, techniques, and libraries. Experience with tasks such as object detection, image classification, segmentation, and feature extraction.
5. Data Handling and Processing: Proficiency in data manipulation, pre-processing, and feature engineering techniques. Experience with tools and libraries for data handling and manipulation, such as Pandas, NumPy, and MATLAB.
6. Software Development Skills: Strong software engineering fundamentals, including proficiency in software design, data structures, algorithms, version control systems, and software testing.
7. Creative Problem-Solving Abilities
8. Good Collaboration and Communication Skills: Abilities to work in cross-functional teams and communicate technical concepts and ideas clearly to both technical and non-technical stakeholders.
9. Domain Knowledge: Not pre-requirement for the company’s specific application domain, but some familiarity with the company’s business background definitely is beneficial.