1,136 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

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.

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

We are seeking a talented and innovative individual to join our team as a Deep Learning Algorithm Specialist. In this role, you will have the opportunity to design and develop cutting-edge AI models that power TikTok's search engine.

You will be working on building a full-stack search engine system based on advanced AI and machine learning methods to provide a world-leading search experience. We value self-direction, intellectual curiosity, openness, and problem-solving, and we're looking for someone who shares these qualities.

• Final year or recent graduate with a background in Computer Science or a related technical field.
• Proficient coding skills and strong algorithmic and data structure knowledge using C++/Python/Java.
• Solid knowledge of machine learning and practical experience in applying it.

• Opportunity to work on real-world projects and make meaningful contributions to the company.
• Collaborative and dynamic work environment.
• Competitive compensation and benefits package.
• Access to cutting-edge technology and tools.

TikTok is a global community where creativity and joy come together. Our mission is to inspire people to express themselves authentically and connect with others. We're passionate about fostering an inclusive and diverse workplace culture that reflects the communities we serve.

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).

Position Summary
To accelerate the adoption of clinical sequencing, Illumina is recruiting a world-class Machine Learning Scientist and Software Engineer to work on the development of novel deep learning algorithms and production-ready software for deciphering the effects of genetic variants in the human genome.
Major aims include modeling the effects of genetic variants on gene function, transcriptional regulation, and diagnosis of pathogenic variants in patients with cancer or rare genetic diseases. A key objective is to develop robust, scalable software implementations of research results and publish these findings in peer-reviewed journals. This will improve the accuracy, throughput, and reproducibility of genome interpretation, thereby removing barriers to the clinical adoption of whole genome sequencing. In addition to strong analytical and software development skills, this position will require initiative, autonomy, and scientific collaboration.
Responsibilities
Contribute to the development of deep learning algorithms for interpreting human genetic data, supporting efforts to identify pathogenic genetic variants using information from clinical phenotypes, protein structures, and genomic data.
Implement, test, and document software modules under the guidance of senior team members, with a focus on reliability, scalability, and efficiency.
Support collaborations with internal and external partners by preparing datasets, running analyses, and contributing to project deliverables.
Assist in preparing research results for internal reports, presentations, and publications, and contribute to integrating methods into software products for the genetics community.
Note: Listed responsibilities are an essential, but not exhaustive, list of usual duties. Changes may occur due to business needs.
Preferred Requirements
Knowledge in deep learning, statistics, bioinformatics, and/or genomics.
Knowledge in full-stack software development and deployment of scientific software.
Familiarity with software development best practices, including version control (e.g., Git), testing frameworks, and CI/CD.
Strong communication skills.
Ability to work in a fast-paced, competitive environment, with a track record of delivering complex scientific projects and publications under tight timelines.
Preferred Experience/Education
BS or MS in computer science, bioinformatics, computational biology, or a related field.
Illumina is an equal opportunity employer committed to providing employment opportunities regardless of sex, race, creed, color, gender, religion, marital status, domestic partner status, age, national origin or ancestry, physical or mental disability, medical condition, sexual orientation, pregnancy, military or veteran status, citizenship status or genetic information. We conduct background checks on applicants whom a conditional offer has been made. The background check process and any decisions will be made in accordance with applicable laws. Illumina prohibits the use of generative AI in the application and interview process. If you require accommodation to complete the application or interview process, please contact
To learn more, visit: The position will be posted until a final candidate is selected or the requisition has a sufficient number of qualified applicants. This role is not eligible for visa sponsorship.
#J-18808-Ljbffr

Cynapse is a pioneering AI software company that specializes in developing cutting-edge Video Intelligence Solutions. Our innovative technology, powered by Generative AI, empowers organizations to enhance safety, operational efficiency, and security in complex environments.

Led by a global team with a proven track record of scaling startups into market leaders, we foster innovation, collaboration, and diverse perspectives. As a leading provider of video intelligence solutions worldwide, Cynapse serves clients globally and redefines what's possible with video analytics.

• This role offers an exciting opportunity to 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.
• 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.

• 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.

• 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).

Duration: Minimum 4 months internship duration, negotiable based on the candidate's schedule.

Artificial Intelligence Vision Expert

We seek a highly skilled Artificial Intelligence Vision Expert to join our team. The ideal candidate will possess in-depth knowledge of deep learning techniques, computer vision algorithms, and software development.

The successful candidate will be responsible for developing, training, and optimizing deep learning models for object detection, classification, and segmentation using real-world datasets.

They will also design and implement software modules to integrate the models into a working system prototype.

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

Overview

Deep Learning Architect, Generative AI Innovation Center at Amazon Web Services (AWS). The GenAI Innovation Center helps AWS customers implement Generative AI solutions and realize transformational business opportunities. The team of strategists, scientists, engineers, and architects works with customers to build bespoke solutions that harness the power of generative AI, imagine and scope high-value use cases, navigate challenges, develop proofs-of-concept, and plan for scalable deployment. The GenAI Innovation Center also provides guidance on best practices for applying generative AI responsibly and cost efficiently. You will design and run experiments, research new algorithms, and explore ways to optimize risk, profitability, and customer experience. We seek top architects, system and software engineers capable of using ML, Generative AI, and related techniques to design, evangelize, implement, and fine-tune state-of-the-art solutions for complex problems.

• Collaborate with AI/ML scientists and architects to research, design, develop, and evaluate generative AI solutions addressing real-world challenges
• Interact with customers directly to understand business problems, assist in implementing generative AI solutions, and guide adoption patterns and paths to production
• Create and deliver best-practice recommendations, tutorials, blog posts, sample code, and presentations for technical, business, and executive stakeholders
• Provide customer and market feedback to product and engineering teams to help define product direction

• Bachelor of Science in Computer Science or related technical field (or equivalent experience); 3+ years of experience designing, building, and/or operating cloud solutions in a production environment
• 2+ years hosting and deploying ML solutions (training, fine-tuning, and inference)
• 2+ years of hands-on experience with Python to build, train, and evaluate models
• 2+ years of technical client engagement experience

• Masters or PhD in computer science or related technical field
• Strong working knowledge of deep learning, machine learning, generative AI, and 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
• Experience building cloud solutions with AWS

We’re committed to an inclusive culture. If you have a disability and need workplace accommodation or adjustments during the application and hiring process, including support for the interview or onboarding, please visit for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.