Machine Learning Engineer

Responsibilities: - Build, evaluate, and deploy machine learning models for fraud and risk detection. - Improve data quality, features, and model performance while balancing fraud detection and customer experience. - Develop AI agents to support investigations, summarize cases, retrieve evidence, and automate workflows. - Use AI coding tools to accelerate development, testing, and debugging and validate generated code. - Work with engineering, product, and risk teams to deploy solutions and monitor their impact. Requirements: - Strong foundation in machine learning with hands-on experience in deep learning and tree-based models (XGBoost/LightGBM). - Strong Python skills and experience training, evaluating, and deploying models. - Practical experience building AI agents or LLM applications (tool calling, retrieval, workflow automation). - Regular use of AI coding tools with the ability to test and improve generated code. - Familiarity with databases and data pipelines for preparing training data and supporting ML workflows. - Good problem-solving, communication, and collaboration skills. Nice to have: - Experience in account takeover (ATO), scam detection, or other fraud and risk use cases. - Experience with LLM training, fine-tuning, evaluation, and alignment. - Experience with reinforcement learning algorithms and practical implementation. - Experience with real-time ML systems, imbalanced datasets, or model explainability. - Prior fraud or risk experience. - Fluency in Mandarin. Benefits: - Competitive total compensation package. - Performance bonus and long-term incentives may be provided. - L&D programs for employee growth and development. - Team building programs and company events. - Wellness and meal allowances. - Comprehensive healthcare schemes for employees and dependants. - Medical Insurance.