Data Scientist – Computer Vision / AI Manipulation Detection

New

BinanceDigital assets

Location: Australia; Hong Kong; Singapore; Taiwan; ThailandFull-TimeSenior

Salary not disclosed

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Job Details

Have a master’s degree or above in Computer Science, Artificial Intelligence, Machine Learning, Computer Vision, or a related field, or equivalent practical experience.
Bring strong computer vision and deep learning expertise, particularly in face recognition, face anti-spoofing, deepfake detection, facial animation detection, or video understanding.
Understand modern deep learning architectures, including CNNs, Vision Transformers, multimodal models, and video-based models.
Be proficient in Python and deep learning frameworks such as PyTorch, with hands-on model training, fine-tuning, and evaluation experience.
Have experience handling large-scale image and video datasets, including preprocessing, noisy labels, data augmentation, and model performance analysis.
Have experience developing and deploying machine learning models in production, focused on accuracy, robustness, scalability, and inference efficiency.
Demonstrate problem-solving, communication, and collaboration skills, and the ability to independently drive complex AI projects.
Biometric authentication, liveness detection, video forgery detection, or adversarial machine learning experience is a plus.
Experience with LLMs, VLMs, agents, knowledge distillation, self-supervised learning, or generative AI is a plus.
Experience with distributed training, GPU optimization using TensorRT, ONNX, or CUDA, or cloud platforms such as AWS SageMaker is a plus.

Research, design, and develop computer vision and deep learning models to detect face spoofing, deepfakes, face swapping, face animation, and AI-generated faces and videos.
Explore image and video modeling techniques, including Vision Transformers, multimodal models, and vision-language models.
Explore applications of LLMs, agents, graph learning, and anomaly detection to risk scenarios.
Build and optimize end-to-end machine learning pipelines for large-scale datasets, model training, evaluation, and deployment.
Improve model accuracy, robustness, and inference efficiency.
Collaborate with KYC, backend engineering, risk operations, and strategy teams to deploy AI solutions into production.
Track advances in computer vision, video understanding, and generative AI, and identify emerging fraud patterns.

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