580 AI Models 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

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.

Position Overview:

Black Sesame Technologies is a rapidly expanding artificial intelligence company backed by substantial VC funding, dedicated to pioneering algorithms and chips for artificial intelligence and image processing. As a Research Scientist in this role, you will collaborate closely with leading researchers to forge cutting-edge perception algorithms aimed at tackling real-world challenges in autonomous driving and beyond. The primary focus of perception is to identify and comprehend the surrounding environment of autonomous vehicles, supporting L2 and L3 automated driving capabilities. This involves constructing a real-time virtual representation of the local environment by processing input data predominantly from cameras, alongside data from complementary sensors such as radars, IMU, and ultrasonic sensors.

Job Description:

• Develop deep learning-based perception algorithms encompassing object detection, lane detection, segmentation, depth estimation, and other related tasks.
• Innovate state-of-the-art network architectures and training methodologies, assessing their efficacy in addressing vision-related challenges.
• Execute deep network compression tailored for ASIC platforms.
• Stay abreast of the latest advancements in deep learning and computer vision literature, proactively proposing novel concepts.
• Contribute to patent and research paper publications.

Job Requirements:

- Master Degree/ PhD in Computer Science, electrical engineering, or a related field.

- Possess 3+ years of experience in computer vision / machine learning development.

- Demonstrated expertise in research and development within one or more of the following domains:

• Deep network development, particularly with visual data.
• Object detection and tracking.
• Stereo / optical flow / depth estimation.
• Multi-view geometry and 3D computer vision.
• Structure from motion.
• Network compression and optimization.
• Network-based sensor fusion.
• Mathematical optimization.
• Proficient programming skills in Python (knowledge of C++ is advantageous).

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.

1. 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.
2. Design, train and deploy biometric models to correctly identify users.
3. 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.
4. Stay updated with the latest research in biometrics, computer vision, and deep learning, incorporating new techniques to improve VIDA’s products.
5. Collaborate with business, product, operations and engineering teams to deliver impact for our customers.
6. Work independently or in a team to solve complex problem statements.

1. 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.
2. Deep understanding of modern computer vision techniques, deep learning and machine learning.
3. Experience developing and deploying machine learning models in production.
4. Experience with adversarial training to enhance model robustness.
5. Proficient in Python, C++, Scala, or Java.
6. Familiarity with modern deep learning frameworks such as TensorFlow, PyTorch, MXNet.
7. Familiarity with cloud platforms like AWS, GCP, or Azure for model deployment.
8. Experience in on-device inference for machine learning models is a plus.
9. Take pride in taking ownership and driving projects to have business impact.
10. 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:

1. 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.
2. Design, train and deploy biometric models to correctly identify users.
3. 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.
4. Stay updated with the latest research in biometrics, computer vision, and deep learning, incorporating new techniques to improve VIDA’s products.
5. Collaborate with business, product, operations and engineering teams to deliver impact for our customers.
6. Work independently or in a team to solve complex problem statements.

Requirements:

1. 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.
2. Deep understanding of modern computer vision techniques, deep learning and machine learning.
3. Experience developing and deploying machine learning models in production.
4. Experience with adversarial training to enhance model robustness.
5. Proficient in Python, C++, Scala, or Java.
6. Familiarity with modern deep learning frameworks such as TensorFlow, PyTorch, MXNet.
7. Familiarity with cloud platforms like AWS, GCP, or Azure for model deployment.
8. Experience in on-device inference for machine learning models is a plus.
9. Take pride in taking ownership and driving projects to have business impact.
10. Thrive in a fast moving collaborative environment.

Are you looking to work at the forefront of Machine Learning and AI? Would you be excited to apply Generative AI algorithms to solve real world problems with significant impact? The Generative AI Innovation Center helps AWS customers implement Generative AI solutions and realize transformational business opportunities. This is a team of strategists, scientists, engineers, and architects working step-by-step with customers to build bespoke solutions that harness the power of generative AI. The team helps customers imagine and scope the use cases that will create the greatest value for their businesses, define paths to navigate technical or business challenges, develop proof-of- concepts, and make plans for launching solutions at scale.

The GenAI Innovation Center team provides guidance on best practices for applying generative AI responsibly and cost efficiently. You will work directly with customers and innovate in a fast-paced organization that contributes to game-changing projects and technologies.

You will design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience.

We're looking for top architects, system and software engineers capable of using ML, Generative AI and other techniques to design, evangelize, implement and fine tune state-of-the- art solutions for never-before-solved problems.

As a Deep Learning Architect, you will

• Collaborate with AI/ML scientists and architects to research, design, develop, and evaluate generative AI solutions to address real-world challenges
• Interact with customers directly to understand their business problems, aid them in implementation of generative AI solutions, brief customers and guide them on adoption patterns and paths to production
• Create and deliver best practice recommendations, tutorials, blog posts, sample code, and presentations adapted to technical, business, and executive stakeholder
• Provide customer and market feedback to product and engineering teams to help define product direction

About the team

Diverse Experiences

AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn't followed a traditional path, or includes alternative experiences, don't let it stop you from applying.

Why AWS?

Amazon Web Services (AWS) is the world's most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that's why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.

Inclusive Team Culture

Here at AWS, it's in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (diversity) conferences, inspire us to never stop embracing our uniqueness.

Mentorship & Career Growth

We're continuously raising our performance bar as we strive to become Earth's Best Employer. That's why you'll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.

Work/Life Balance

We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there's nothing we can't achieve in the cloud.

BASIC QUALIFICATIONS

• Bachelor of Science degree in Computer Science, or related technical, math, or scientific field (or equivalent experience)
• 5+ years of experience in designing, building, and/or operating cloud solutions in a production environment
• 4+ years experience hosting and deploying ML solutions (e.g., for training, fine tuning, and inference)
• 4+ years of hands on experience with Python to build, train, and evaluate models
• 4+ years of technical client engagement experience

PREFERRED QUALIFICATIONS

• Masters or PhD degree in computer science, or related technical, math, or scientific field
• 3+ years experience working with deep learning, machine learning, generative AI, or statistics
• Experience building generative AI applications on AWS using services such as Amazon Bedrock and Amazon SageMaker.
• Experience communicating across technical and non-technical audiences, including executive level stakeholders or clients
• Experience building cloud solutions with AWS

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit for more information. If the country/region you're applying in isn't listed, please contact your Recruiting Partner.

Job details

SGP, Singapore

Data Science

Are you looking to work at the forefront of Machine Learning and AI? Would you be excited to apply Generative AI algorithms to solve real world problems with significant impact? The Generative AI Innovation Center helps AWS customers implement Generative AI solutions and realize transformational business opportunities. This is a team of strategists, scientists, engineers, and architects working step-by-step with customers to build bespoke solutions that harness the power of generative AI. The team helps customers imagine and scope the use cases that will create the greatest value for their businesses, define paths to navigate technical or business challenges, develop proof-of- concepts, and make plans for launching solutions at scale.

The GenAI Innovation Center team provides guidance on best practices for applying generative AI responsibly and cost efficiently. You will work directly with customers and innovate in a fast-paced organization that contributes to game-changing projects and technologies.

You will design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience.

We're looking for top architects, system and software engineers capable of using ML, Generative AI and other techniques to design, evangelize, implement and fine tune state-of-the- art solutions for never-before-solved problems.

As a Deep Learning Architect, you will

• Collaborate with AI/ML scientists and architects to research, design, develop, and evaluate generative AI solutions to address real-world challenges
• Interact with customers directly to understand their business problems, aid them in implementation of generative AI solutions, brief customers and guide them on adoption patterns and paths to production
• Create and deliver best practice recommendations, tutorials, blog posts, sample code, and presentations adapted to technical, business, and executive stakeholder
• Provide customer and market feedback to product and engineering teams to help define product direction

About the team

Diverse Experiences

AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn't followed a traditional path, or includes alternative experiences, don't let it stop you from applying.

Why AWS?

Amazon Web Services (AWS) is the world's most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that's why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.

Inclusive Team Culture

Here at AWS, it's in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (diversity) conferences, inspire us to never stop embracing our uniqueness.

Mentorship & Career Growth

We're continuously raising our performance bar as we strive to become Earth's Best Employer. That's why you'll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.

Work/Life Balance

We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there's nothing we can't achieve in the cloud.

• Bachelor of Science degree in Computer Science, or related technical, math, or scientific field (or equivalent experience)
• 5+ years of experience in designing, building, and/or operating cloud solutions in a production environment
• 4+ years experience hosting and deploying ML solutions (e.g., for training, fine tuning, and inference)
• 4+ years of hands on experience with Python to build, train, and evaluate models
• 4+ years of technical client engagement experience

• Masters or PhD degree in computer science, or related technical, math, or scientific field
• 3+ years experience working with deep learning, machine learning, generative AI, or statistics
• Experience building generative AI applications on AWS using services such as Amazon Bedrock and Amazon SageMaker.
• Experience communicating across technical and non-technical audiences, including executive level stakeholders or clients
• Experience building cloud solutions with AWS

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit for more information. If the country/region you're applying in isn't listed, please contact your Recruiting Partner.