945 Machine Learning Engineers jobs in Singapore

Implement AI models for 3D data generation using deep learning techniques. Strong experience in 3D computer vision and 3D graphics required. Responsibilities include data preprocessing and feature engineering. Expert in GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders).
LOCATION
EMPLOYMENT TYPE
Permanent
What You’ll Do
Developing and implementing deep learning models for 3D data, such as point clouds, voxel grids, and triangle meshes.
Training and fine-tuning deep learning models on large datasets of 3D data, such as 3D scans of real-world objects and environments.
Using deep learning to extract features and representations from 3D data, such as object segmentation, surface normal estimation, and 3D object recognition.
Researching and implementing new techniques for 3D deep learning, such as point cloud convolutional neural networks and volumetric convolutional neural networks.
Collaborating with other engineers and researchers to integrate 3D deep learning models into other systems, such as robotics and autonomous vehicles.
Building and deploying 3D deep learning models on various platforms, including cloud, edge, and mobile devices.
Optimizing the performance of 3D deep learning models, including reducing memory and computational requirements and improving inference speed.
Communicating the results of their research and development activities to stakeholders and customers, including technical and non-technical audiences.
Keeping up with current research and developments in the field of 3D deep learning and identifying new opportunities for applying 3D deep learning to real-world problems.
Utilizing NVIDIA's deep learning frameworks, such as CUDA, cuDNN, and TensorRT, to optimize the performance of 3D deep learning models.
Using NVIDIA's Jetson platform for deploying deep learning models on edge devices.
Using NVIDIA's GPU-accelerated cloud platforms, such as NVIDIA GPU Cloud (NGC), for training and deploying deep learning models in the cloud.
Leveraging NVIDIA's AI-specific hardware, such as the NVIDIA A100 Tensor Core GPU, for faster and more efficient training and inference of deep learning models.
Utilizing NVIDIA's AI development tools, such as DeepStream and Isaac, to develop and deploy AI-powered applications for robotics and autonomous systems.
Using NVIDIA's Clara platform for medical imaging and other 3D data analysis.
Utilizing NVIDIA's Omniverse platform for creating and training models in virtual environments.
Who You Are
Strong technical skills: A 3D Deep Learning Engineer should have a solid understanding of deep learning and computer vision, as well as experience with programming languages such as Python and C++.
Experience with 3D data: You should have experience working with 3D data, such as point clouds, voxel grids, and triangle meshes, and should be familiar with the techniques and algorithms used for processing 3D data.
Experience with deep learning frameworks: You should have experience working with deep learning frameworks, such as TensorFlow, PyTorch, and NVIDIA's CUDA, cuDNN, and TensorRT, and should be familiar with the techniques used to optimize the performance of deep learning models.
Strong problem-solving skills: You should have strong problem-solving skills and be able to develop creative solutions to complex technical challenges.
Attention to detail: You should be meticulous and pay attention to detail, as small errors or bugs in the code can cause significant problems.
Strong communication skills: You should be able to effectively communicate technical ideas and solutions to both technical and non-technical audiences.
Flexibility and Adaptability: You should be able to adapt and learn quickly as the field of AI is constantly evolving and new techniques and technologies are emerging.
Creativity: You should have a creative mindset and be able to come up with new and innovative solutions to problems.
Team Player: You should have good collaboration skills and be able to work well in a team environment.
Passion for technology: You should have a genuine passion for technology and a desire to learn and stay current with the latest developments in the field.
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Deep Learning Research Scientist

We are seeking a highly skilled and innovative Deep Learning Research Scientist to pioneer AI-driven approaches in drug discovery, leveraging unique datasets and cutting-edge technologies.

• Develop and train advanced deep learning models to transform the drug development process.
• Work with large multimodal datasets, including drug screening results, genomic data, and electronic health records.
• Contribute to the discovery of new antibiotics and the design of novel diagnostic tests.
• Collaborate with experts in biology and computer science to advance our research goals.
• Publish research findings in leading scientific journals.

• Bachelor's or Master's degree in Computer Science, Bioinformatics, or a related field.
• Proven experience in training large deep learning models using popular frameworks like TensorFlow or PyTorch.
• Familiarity with reinforcement learning, generative AI, or statistical modeling is a significant advantage.
• Experience working with large biological datasets or healthcare records is desirable, but a strong willingness to learn is equally valued.

Machine Learning, Cell Biology, Hospitals, Statistical Modeling, DNA, Treatment, Healthcare, Medicine, Career Development, Biology, Drug Discovery, Bioinformatics

The successful candidate will have the opportunity to work on cutting-edge projects, collaborate with world-renowned experts, and publish research findings in top-tier scientific journals. We offer a dynamic and supportive work environment, competitive salary, and opportunities for career growth and professional development.

As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the opportunity to be equipped with applied research skill sets that are relevant to industry demands while working on research projects in SIT.

The primary responsibility of this role is to support and contribute to an industry innovation research project. The Research Engineer will work closely with the Principal Investigator (PI), Co-PI, and the research team to develop deep learning-based computer vision algorithms and software for object detection, classification, and segmentation.

Key Responsibilities:

• Participate in and manage the research project together with the PI, Co-PI, and research team to ensure timely achievement of project deliverables.
• Undertake the following specific responsibilities in the project:

1. Develop, train, and optimise deep learning models for object detection, classification, and segmentation using real-world datasets.
2. Design and implement software modules to integrate the models into a working system prototype.
3. Perform data annotation.
4. Conduct experiments, analyse results, and iterate models for improved accuracy and efficiency.
5. Prepare project documentation, technical reports, and academic publications.
6. Collaborate with industry partners and contribute to technology transfer efforts.

Job Requirements:

• Possess strong technical knowledge and hands-on experience in:

1. Deep learning frameworks (e.g., PyTorch, TensorFlow)
2. Deep learning models (e.g., YOLO, U-Net, EfficientNet, ResNet, FPN, Fast R-CNN)
3. Computer vision techniques and algorithms
4. Python and relevant libraries (e.g., PyQt, OpenCV, NumPy, scikit-learn), particularly for developing Windows desktop application software incorporating deep learning models

• Hold at least a Bachelor's degree in Computer Science, Electrical/Electronic/Software Engineering, or a related field. A Master's or PhD degree in relevant areas will be advantageous.
• Familiarity with the following areas is advantageous:

1. Participation in Kaggle competitions, showcasing practical problem-solving and model development skills
2. Model deployment (e.g., ONNX, TensorRT)
3. Edge computing or embedded vision systems (e.g., NVIDIA Jetson Nano)
4. Real-time processing and GPU acceleration
5. Experience working on industry R&D project

Key Competencies:

• Able to build and maintain strong working relationships with team members, stakeholders, and external partners
• Self-motivated and committed to continuous learning and improvement
• Proficient in technical writing & presentation, research reporting, and academic publication
• Possess strong analytical, problem-solving, and critical thinking skills
• Demonstrate initiative and ownership in carrying out tasks independently

As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the opportunity to be equipped with applied research skill sets that are relevant to industry demands while working on research projects in SIT.

The primary responsibility of this role is to support and contribute to an industry innovation research project. The Research Engineer will work closely with the Principal Investigator (PI), Co-PI, and the research team to develop deep learning-based computer vision algorithms and software for object detection, classification, and segmentation.

Key Responsibilities:

• Participate in and manage the research project together with the PI, Co-PI, and research team to ensure timely achievement of project deliverables.
• Undertake the following specific responsibilities in the project:

1. Develop, train, and optimise deep learning models for object detection, classification, and segmentation using real-world datasets.
2. Design and implement software modules to integrate the models into a working system prototype.
3. Perform data annotation.
4. Conduct experiments, analyse results, and iterate models for improved accuracy and efficiency.
5. Prepare project documentation, technical reports, and academic publications.
6. Collaborate with industry partners and contribute to technology transfer efforts.

Job Requirements:

• Possess strong technical knowledge and hands-on experience in:

1. Deep learning frameworks (e.g., PyTorch, TensorFlow)
2. Deep learning models (e.g., YOLO, U-Net, EfficientNet, ResNet, FPN, Fast R-CNN)
3. Computer vision techniques and algorithms
4. Python and relevant libraries (e.g., PyQt, OpenCV, NumPy, scikit-learn), particularly for developing Windows desktop application software incorporating deep learning models

• Hold at least a Bachelor’s degree in Computer Science, Electrical/Electronic/Software Engineering, or a related field. A Master’s or PhD degree in relevant areas will be advantageous.
• Familiarity with the following areas is advantageous:

1. Participation in Kaggle competitions, showcasing practical problem-solving and model development skills
2. Model deployment (e.g., ONNX, TensorRT)
3. Edge computing or embedded vision systems (e.g., NVIDIA Jetson Nano)
4. Real-time processing and GPU acceleration
5. Experience working on industry R&D project

Key Competencies:

• Able to build and maintain strong working relationships with team members, stakeholders, and external partners
• Self-motivated and committed to continuous learning and improvement
• Proficient in technical writing & presentation, research reporting, and academic publication
• Possess strong analytical, problem-solving, and critical thinking skills
• Demonstrate initiative and ownership in carrying out tasks independently

About Cynapse

Cynapse is a leading AI software company specializing in enterprise-grade Video Intelligence Solutions Powered by Generative AI, tailored to meet the unique challenges of various industries. Our vertical-specific solutions empower organizations to enhance safety, operational efficiency, and security in complex environments such as roads, seaports, airports, and cities. By combining advanced Vision AI with Generative AI, we continually push the boundaries of video analytics, delivering insights and automation that transform operations.

Led by a global team with a proven track record of scaling startups into market leaders, we foster innovation, collaboration, and diverse perspectives. Headquartered from US, Cynapse serves clients worldwide, redefining what's possible with video intelligence.

Job Description

We are looking for an AI Engineering Intern (Computer Vision & Deep Learning) to join our Computer Vision Model Engineering Team. This is a unique opportunity to contribute to the development and deployment of cutting-edge AI models by integrating deep learning, software engineering practices, and ML pipelines.

You'll work alongside a dynamic team, gaining hands-on experience and contributing to real-world AI projects that optimize the machine learning lifecycle.

As an AI Engineering Intern, you will:

• Gain hands-on experience across the entire deep learning pipeline, including data preparation, model training, evaluation, and deployment.
• Work closely with engineers to design, build, and refine deep learning models for various Computer Vision tasks, such as image classification, segmentation, object detection, action recognition, and more.
• Work with ML pipelines that support model deployment, assisting with tasks like model integration, data versioning , automated training, and testing.
• Contribute to the optimization of models and pipelines by conducting experiments, analyzing
• results, and helping to improve model performance.
• Gain exposure to modern tools like Docker, CI/CD , and cloud platforms to support scalable, reliable model deployment.
• Dive into cutting-edge research and gain insights into model architecture optimization, scalability, and challenges related to real-time application in Computer Vision.

Requirements:

• Currently pursuing or completed a Diploma/ Bachelor/ Master's in Computer Science, Data Science, Artificial Intelligence, Machine Learning, or a related field.
• Basic understanding of machine learning concepts, demonstrated through the completion of at least one hands-on university or online module.
• Proficiency in Python programming (minimum 6 months of hands-on experience or equivalent AI/ML project experience, including GitHub contributions).
• Familiarity with at least one deep learning framework such as TensorFlow, PyTorch , or similar.
• Strong analytical thinking and problem-solving skills , with an emphasis on improving software pipelines .

Preferred (Bonus Skills):

• Familiarity with building and optimizing ML pipelines , including data preprocessing, model training, testing , and deployment .
• Experience in software engineering practices like version control (Git), CI/CD pipelines , or Docker for containerization.
• Exposure to cloud platforms (AWS, GCP, Azure) for deploying and managing ML models in production.
• Knowledge of computer vision concepts and libraries (e.g., OpenCV).
• Interest in exploring advanced topics like Generative AI, multi-modal learning , or real-time video analytics .

Duration:

• Internship duration: Minimum 4 months (negotiable based on the candidate's schedule).
• Availability: At least 4 days a week , with a preference for full-time commitment.
• Flexible start and end dates to accommodate exams or personal schedules.

Note : Due to the nature of the role, candidates must be based in Singapore or have relevant experience studying/working in Singapore.

Organisation/Company SINGAPORE INSTITUTE OF TECHNOLOGY (SIT) Research Field Computer science Engineering Researcher Profile Recognised Researcher (R2) First Stage Researcher (R1) Country Singapore Application Deadline 24 Sep 2025 - 00:00 (UTC) Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No
Offer Description
As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the opportunity to be equipped with applied research skill sets that are relevant to industry demands while working on research projects in SIT.
The primary responsibility of this role is to support and contribute to an industry innovation research project. The Research Engineer will work closely with the Principal Investigator (PI), Co-PI, and the research team to develop deep learning-based computer vision algorithms and software for object detection, classification, and segmentation.
Key Responsibilities
Participate in and manage the research project together with the PI, Co-PI, and research team to ensure timely achievement of project deliverables.
Undertake the following specific responsibilities in the project:
i. Develop, train, and optimise deep learning models for object detection, classification, and segmentation using real-world datasets.
ii.Design and implement software modules to integrate the models into a working system prototype.
iii. Perform data annotation.
iv. Conduct experiments, analyse results, and iterate models for improved accuracy and efficiency.
v. Prepare project documentation, technical reports, and academic publications.
vi.Collaborate with industry partners and contribute to technology transfer efforts.
Job Requirements
1. Possess strong technical knowledge and hands-on experience in:
Deep learning frameworks (e.g., PyTorch, TensorFlow)
Deep learning models (e.g., YOLO, U-Net, EfficientNet, ResNet, FPN, Fast R-CNN)
Computer vision techniques and algorithms
Python and relevant libraries (e.g., PyQt, OpenCV, NumPy, scikit-learn), particularly for developing Windows desktop application software incorporating deep learning models
2. Hold at least a Bachelor’s degree in Computer Science, Electrical/Electronic/Software Engineering, or a related field.
A Master’s or PhD degree in relevant areas will be advantageous.
3. Familiarity with the following areas is advantageous:
Participation in Kaggle competitions, showcasing practical problem-solving and model development skills
Edge computing or embedded vision systems (e.g., NVIDIA Jetson Nano)
Real-time processing and GPU acceleration
Experience working on industry R&D projects
Able to build and maintain strong working relationships with team members, stakeholders, and external partners
Self-motivated and committed to continuous learning and improvement
Proficient in technical writing & presentation, research reporting, and academic publication
Possess strong analytical, problem-solving, and critical thinking skills
Demonstrate initiative and ownership in carrying out tasks independently
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(Job ID: )

Responsibilities:

• Design, develop, and optimize computer vision algorithms for defect detection, feature recognition, and automation in semiconductor manufacturing.
• Implement deep learning-based vision models for high-speed and high-accuracy inspection processes.
• Integrate vision systems with robotics, automation platforms, and semiconductor backend equipment (e.g., wire bonders, die bonders).
• Develop and fine-tune image processing techniques for pattern recognition, object tracking, and quality inspection.
• Work closely with cross-functional teams to enhance real-time vision inspection and improve production yield.
• Conduct data analysis, model validation, and performance tuning to meet industry standards.
• Optimize vision system performance in terms of latency, accuracy, and robustness.

Requirements:

• Strong experience in computer vision, image processing, and machine learning.
• Proficiency in Python, C++, OpenCV, TensorFlow, or PyTorch for vision-related applications.
• Experience with deep learning models (CNNs, GANs, Transformers) for vision tasks is an added advantage.
• Familiarity with semiconductor automation, optical inspection, or metrology is advantageous.
• Knowledge of real-time processing, embedded systems, and hardware acceleration (CUDA, FPGA, etc.) is an added advantage.

To apply, kindly send your updated resume to

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However, rest assured that all applications will be updated to our resume bank for future opportunities.

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EA Licence No.: 07C5771

EA Personnel Reg. No.: R

EA Personnel Name: Edmund Ting Chao Siong

Our mission is to create innovative, robust, and user-friendly digital identity solutions. We are looking for a passionate and skilled Deep Learning AI Scientist specializing in Liveness Detection and Biometrics to join our dynamic team. Your work will directly impact the security and reliability of VIDA's identity verification systems.

Responsibilities:

• Liveness Detection Development: Design, train, and deploy advanced deep learning models to ensure robust liveness detection, preventing spoofing attacks using photos, videos, masks, or other methods.

• Design, train and deploy biometric models to correctly identify users

• Own the full lifecycle of deploying models: from data labelling, working with engineers to design scalable APIs, to monitoring and A/B testing new model versions

• Stay updated with the latest research in biometrics, computer vision, and deep learning, incorporating new techniques to improve VIDA’s products

• Collaborate with business, product, operations and engineering teams to deliver impact for our customers

• Work independently or in a team to solve complex problem statements

Requirements:

• An advanced degree in a quantitative field, and 3+ years of hands-on experience in deep learning model development for biometrics or liveness detection or a similar field.

• Deep understanding of modern computer vision techniques, deep learning and machine learning

• Experience developing and deploying machine learning models in production

• Experience with adversarial training to enhance model robustness

• Proficient in Python, C++, Scala, or Java

• Familiarity with modern deep learning frameworks such as TensorFlow, PyTorch, MXNet

• Familiarity with cloud platforms like AWS, GCP, or Azure for model deployment.

• Experience in on-device inference for machine learning models is a plus

• Take pride in taking ownership and driving projects to have business impact

• Thrive in a fast moving collaborative environment

What are we trying to solve?

We have 7.5 billion people on Earth, of which over 1 billion cannot securely prove their identity right now.

Every year, 140 million babies are born, of which 40 million go unregistered.

Simply put, these people are deprived of social benefits, such as education and health, their civil rights to vote and travel; and are excluded from the economy because they cannot sign up for bank accounts, loans, welfare programs etc. We believe this is unacceptable, and needs to change.

At VIDA , We are creating a frictionless digital identity system. One that fulfills the needs and expectations of our times, and is available anywhere, for everyone.

Why are we solving this problem?

The United Nations (UN) and World Bank ID4D initiatives aim to provide everyone on the planet with a legal identity by 2030. This deadline is just 9 years away, we are expecting a digital identity to be a legal human right by then and we at VIDA want to be pioneers in leading this change.

Who are we?

We are a highly driven bunch of people to solve this problem for our own reasons. Whether it is to solve for misleading doctors, or because we didn’t get access to fair ration due to corruption - Our collective goal aligns.

Our mission is to create innovative, robust, and user-friendly digital identity solutions. We are looking for a passionate and skilled Deep Learning AI Scientist specializing in Liveness Detection and Biometrics to join our dynamic team. Your work will directly impact the security and reliability of VIDA's identity verification systems.
Responsibilities:
Liveness Detection Development: Design, train, and deploy advanced deep learning models to ensure robust liveness detection, preventing spoofing attacks using photos, videos, masks, or other methods.
Design, train and deploy biometric models to correctly identify users
Own the full lifecycle of deploying models: from data labelling, working with engineers to design scalable APIs, to monitoring and A/B testing new model versions
Stay updated with the latest research in biometrics, computer vision, and deep learning, incorporating new techniques to improve VIDA’s products
Collaborate with business, product, operations and engineering teams to deliver impact for our customers
Work independently or in a team to solve complex problem statements
Requirements:
An advanced degree in a quantitative field, and 3+ years of hands-on experience in deep learning model development for biometrics or liveness detection or a similar field.
Deep understanding of modern computer vision techniques, deep learning and machine learning
Experience developing and deploying machine learning models in production
Experience with adversarial training to enhance model robustness
Proficient in Python, C++, Scala, or Java
Familiarity with modern deep learning frameworks such as TensorFlow, PyTorch, MXNet
Familiarity with cloud platforms like AWS, GCP, or Azure for model deployment.
Experience in on-device inference for machine learning models is a plus
Take pride in taking ownership and driving projects to have business impact
Thrive in a fast moving collaborative environment
What are we trying to solve?
We have 7.5 billion people on Earth, of which over 1 billion cannot securely prove their identity right now.
Every year, 140 million babies are born, of which 40 million go unregistered.
Simply put, these people are deprived of social benefits, such as education and health, their civil rights to vote and travel; and are excluded from the economy because they cannot sign up for bank accounts, loans, welfare programs etc. We believe this is unacceptable, and needs to change.
AtVIDA, We are creating a frictionless digital identity system. One that fulfills the needs and expectations of our times, and is available anywhere, for everyone.
Why are we solving this problem?
The United Nations (UN) and World Bank ID4D initiatives aim to provide everyone on the planet with a legal identity by 2030. This deadline is just 9 years away, we are expecting a digital identity to be a legal human right by then and we at VIDA want to be pioneers in leading this change.
Who are we?
We are a highly driven bunch of people to solve this problem for our own reasons. Whether it is to solve for misleading doctors, or because we didn’t get access to fair ration due to corruption - Our collective goal aligns.
#J-18808-Ljbffr