Job Description
The mission of this role is to comprehensively enhance the exchange's risk prevention and automated analysis capabilities through hybrid architecture, while contributing to the development of the next-generation "AI-Ready" risk control system.
Key Responsibilities
- Fraud Network Mining and Graph Analysis: Utilize graph neural networks (GNN) and other algorithms to analyze massive trading behaviors and on-chain data, identifying and monitoring online fraud intelligence and high-risk address networks.
- Risk Control Strategy Assistance and Risk Prediction: Leverage AI to assist in organizing risk control rules, optimizing strategies, automatically identifying vulnerabilities in existing risk control rules, and developing forward-looking risk prediction models to prevent potential threats.
- Market Manipulation and Irregular Trading Detection: Build high-concurrency, low-latency real-time monitoring and interception models for abnormal trading behaviors such as wash trading, matched orders, spoofing, pump & dump, and front-running.
- Real-Time Anti-Fraud and Hybrid Architecture: Establish and optimize real-time risk control pipelines combining traditional machine learning and large model intent recognition to accurately intercept P2P fraud and abnormal trading groups, reducing financial losses.
- Automated Risk Control Material Review: Develop multimodal review pipelines to achieve automatic material parsing and cross-validation.
Note: We are hiring for two directions—AI Risk Control Algorithm Engineer and AI Large Model Infrastructure Algorithm Engineer. Specific details can be discussed via Telegram.
Job Requirements
- Bachelor's degree or higher in Computer Science, Statistics, Mathematics, or related fields (Master's preferred), with 5+ years of experience in risk control algorithms.
- Strong data sensitivity and the ability to independently define problems and drive implementation.
- Proficiency in Python and SQL, with experience in large-scale data processing (Hive/Spark).
- Solid foundation in machine learning, familiar with feature engineering and end-to-end model optimization.
- Experience with graph algorithms in fraud network mining and group identification.
- Knowledge of sequence models for behavioral anomaly detection.
- Experience with real-time systems (Flink/Kafka) and understanding of online inference pipeline design.
- Familiarity with at least one of the following business logics and adversarial evolution scenarios:
- Fraud countermeasures (e.g., bonus abuse, fake accounts, bulk attacks)
- Trading surveillance (e.g., wash trading, spoofing, pump & dump)
- P2P anti-fraud / AML compliance
Preferred Qualifications
- Experience in on-chain address analysis and fund tracing (e.g., Chainalysis, TRM).
- Practical experience in LLM-based risk control (e.g., intent recognition, multimodal review, RAG-based Q&A, workflow orchestration).
- Experience in designing risk control rule engines or strategy platforms.
- Understanding of order book mechanisms and market microstructure.
- Background in risk control at top-tier exchanges or large fintech platforms.
- Publications in top conferences such as KDD, AAAI, or WWW.
Benefits
Negotiable
