Data Scientist – Computer Vision / AI Manipulation Detection (Risk AI)

Location: Australia, Hong Kong, Singapore, Taiwan, or Thailand. Remote-first, work-from-home arrangement may vary depending on the role and team.

Company

Binance is a global blockchain ecosystem operating a cryptocurrency exchange and digital-asset products, with a focus on security, transparency, trading, finance, payments, research, and education.

What you will do

• Research, design, develop, and deploy computer vision and deep learning models for detecting face spoofing, deepfakes, face swapping, facial animation, AI-generated faces and videos, and other identity attacks.
• Explore image and video modeling approaches including Vision Transformers, multimodal models, and vision-language models.
• Investigate applications of LLMs, agents, graph learning, and anomaly detection to automated investigation, risk reasoning, and real-time monitoring.
• Build and optimize end-to-end machine learning pipelines for large-scale datasets, noisy labels, model training, evaluation, and deployment.
• Improve model accuracy, robustness, scalability, and inference efficiency while collaborating with KYC, backend engineering, risk operations, and strategy teams.
• Identify emerging fraud patterns and translate computer vision, video understanding, and generative AI research into production risk-control solutions.

Requirements

• Master’s degree or above in Computer Science, Artificial Intelligence, Machine Learning, Computer Vision, or a related field, or equivalent practical experience.
• Strong expertise in computer vision and deep learning, particularly face recognition, face anti-spoofing, deepfake detection, facial animation detection, or video understanding.
• Solid understanding of CNNs, Vision Transformers, multimodal models, and video-based models.
• Proficiency in Python and deep learning frameworks such as PyTorch, with hands-on experience in model training, fine-tuning, and evaluation.
• Experience handling large-scale image and video datasets, including preprocessing, noisy labels, data augmentation, and model performance analysis.
• Experience developing and deploying machine learning models in production, with focus on accuracy, robustness, scalability, and inference efficiency.
• Strong problem-solving, communication, and collaboration skills, with the ability to independently drive complex AI projects.

Nice to have

• Extensive experience in biometric authentication, liveness detection, deepfake detection, video forgery detection, or adversarial machine learning.
• Hands-on experience with LLMs, VLMs, agents, knowledge distillation, self-supervised learning, or generative AI.
• Experience with large-scale ML infrastructure, distributed training, GPU optimization using TensorRT, ONNX, or CUDA, or cloud platforms such as AWS SageMaker.
• Publications at CVPR, ICCV, ECCV, NeurIPS, ICML, or ICLR; notable open-source contributions; or experience in identity verification, fraud detection, or AI security.

Culture & Benefits

• Remote-first, work-from-home arrangement, subject to the nature of the role and team.
• Global, user-centric organization with a flat structure and collaboration with world-class talent.
• Fast-paced, innovative, results-driven environment with autonomy and challenging projects.
• Career growth and continuous learning opportunities.
• Competitive salary and company benefits.