900 Senior AI Engineers jobs in Singapore

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RSS3 is a decentralized network indexing and structuring open information, making it accessible and valuable for the next Twitter, Google, and OpenAI.

We advocate for the power of freedom of information and emphasize its openness, accessibility and universal availability. Unlike major platforms such as Twitter, Facebook, Instagram, and TikTok that control vast amounts of public information, we urge individuals to redirect the power of data giants toward the public good by supporting decentralized channels and networks, and envision a world where open information empowers everyone.

RSS3 was launched by Natural Selection Labs and is backed by top investors including Arweave, Coinbase, Coinshares, Dapper Labs, Dragonfly, Fabric Ventures and Hashkey.

We are a globally distributed organization: all of our members come from different countries, including US, Canada, UK, China, Singapore, New Zealand, and Australia.

Our team is equal, open, and free . We advocate flexible working hours , so both early birds and night owls are welcome!

Since our establishment, we have made significant progress and are incredibly proud of our team culture and atmosphere.

In this role, you'll embark on an exciting journey to harness the potential of decentralized data. You'll be creating and improving state-of-the-art Web3 x AI solutions using decentralized data, driving the acceleration of Internet decentralization. Your contributions will shape the future of how everyone interacts with information, opening up avenues to democratize access to decentralized networks.

• Recognize our team culture and value
• Deep understanding of Machine Learning and Deep Learning
• Experience in Open LLMs such as Llama/Mistral/GLM
• Experience in frameworks such as LangChain
• Experience in vector databases such as Milvus/Chroma
• Mastery of software engineering principles
• Strong communication and collaboration skills

• Prior experience in fully-remote roles
• Understanding of Web3 and blockchain technology
• Insightful views of cutting-edge Open LLM models

• Full-time
• Remote

• A chance to directly participate in the Internet revolution and make the world a better place.
• Opportunities to collaborate with the world’s top Open Web projects.
• A team of loving and competent companions!
• FULLY-REMOTE role with very flexible working hours.
• An AWESOME & CARING company culture.
• Competitive allowances and benefits.
• We are committed to diversity and welcome talented applicants from all walks of life to apply! All applicants will be considered regardless of race, color, religion, gender identity, sexual orientation, nationality, veteran or disability status.

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(Remote) RSS3 is looking for AI engineers #415
RSS3 is a decentralized network indexing and structuring open information, making it accessible and valuable for the next Twitter, Google, and OpenAI.
We advocate for the power of freedom of information and emphasize its openness, accessibility and universal availability. Unlike major platforms such as Twitter, Facebook, Instagram, and TikTok that control vast amounts of public information, we urge individuals to redirect the power of data giants toward the public good by supporting decentralized channels and networks, and envision a world where open information empowers everyone.
RSS3 was launched by Natural Selection Labs and is backed by top investors including Arweave, Coinbase, Coinshares, Dapper Labs, Dragonfly, Fabric Ventures and Hashkey.
We are a globally distributed organization: all of our members come from different countries, including US, Canada, UK, China, Singapore, New Zealand, and Australia.
Our team is
equal, open, and free . We advocate
flexible
working hours , so both early birds and night owls are welcome!
Since our establishment, we have made significant progress and are incredibly proud of our team culture and atmosphere.
Responsibilities
In this role, you'll embark on an exciting journey to harness the potential of decentralized data. You'll be creating and improving state-of-the-art Web3 x AI solutions using decentralized data, driving the acceleration of Internet decentralization. Your contributions will shape the future of how everyone interacts with information, opening up avenues to democratize access to decentralized networks.
We want you to:
Refine and optimize Language User Interface (LUI) products
Leverage MoE/CoT frameworks to push boundaries
Design and implement modular and open source AI components using decentralized data
Play a pivotal role in driving the decentralization of the Internet
Requirements
Recognize our team culture and value
Deep understanding of Machine Learning and Deep Learning
Experience in Open LLMs such as Llama/Mistral/GLM
Experience in frameworks such as LangChain
Experience in vector databases such as Milvus/Chroma
Mastery of software engineering principles
Strong communication and collaboration skills
Nice to Haves
Prior experience in fully-remote roles
Understanding of Web3 and blockchain technology
Insightful views of cutting-edge Open LLM models
Job Nature
Full-time
Remote
We offer:
A chance to directly participate in the Internet revolution and make the world a better place.
Opportunities to collaborate with the world’s top Open Web projects.
A team of loving and competent companions!
FULLY-REMOTE
role with very flexible working hours.
An
AWESOME & CARING
company culture.
Competitive allowances and benefits.
We are committed to diversity and welcome talented applicants from all walks of life to apply! All applicants will be considered regardless of race, color, religion, gender identity, sexual orientation, nationality, veteran or disability status.
#J-18808-Ljbffr

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

1. Developing and implementing deep learning models for 3D data, such as point clouds, voxel grids, and triangle meshes.
2. Training and fine-tuning deep learning models on large datasets of 3D data, such as 3D scans of real-world objects and environments.
3. Using deep learning to extract features and representations from 3D data, such as object segmentation, surface normal estimation, and 3D object recognition.
4. Researching and implementing new techniques for 3D deep learning, such as point cloud convolutional neural networks and volumetric convolutional neural networks.
5. Collaborating with other engineers and researchers to integrate 3D deep learning models into other systems, such as robotics and autonomous vehicles.
6. Building and deploying 3D deep learning models on various platforms, including cloud, edge, and mobile devices.
7. Optimizing the performance of 3D deep learning models, including reducing memory and computational requirements and improving inference speed.
8. Communicating the results of their research and development activities to stakeholders and customers, including technical and non-technical audiences.
9. 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.
10. Utilizing NVIDIA's deep learning frameworks, such as CUDA, cuDNN, and TensorRT, to optimize the performance of 3D deep learning models.
11. Using NVIDIA's Jetson platform for deploying deep learning models on edge devices.
12. Using NVIDIA's GPU-accelerated cloud platforms, such as NVIDIA GPU Cloud (NGC), for training and deploying deep learning models in the cloud.
13. 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.
14. Utilizing NVIDIA's AI development tools, such as DeepStream and Isaac, to develop and deploy AI-powered applications for robotics and autonomous systems.
15. Using NVIDIA's Clara platform for medical imaging and other 3D data analysis.
16. Utilizing NVIDIA's Omniverse platform for creating and training models in virtual environments.

1. 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++.
2. 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.
3. 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.
4. Strong problem-solving skills: You should have strong problem-solving skills and be able to develop creative solutions to complex technical challenges.
5. Attention to detail: You should be meticulous and pay attention to detail, as small errors or bugs in the code can cause significant problems.
6. Strong communication skills: You should be able to effectively communicate technical ideas and solutions to both technical and non-technical audiences.
7. 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.
8. Creativity: You should have a creative mindset and be able to come up with new and innovative solutions to problems.
9. Team Player: You should have good collaboration skills and be able to work well in a team environment.
10. 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.

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.

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.
#J-18808-Ljbffr

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.

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.