Job Description
Mission: Leverage hybrid architecture to comprehensively enhance the exchange's risk defense and automated analysis capabilities, while contributing to the development of the next-generation "AI-Ready" risk control system.
Key Responsibilities:
- Fraud Network Mining & 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 & Prediction: Use AI to assist in risk rule refinement and strategy optimization, automatically identifying vulnerabilities in existing risk control rules, and developing forward-looking risk prediction models for proactive prevention.
- Market Manipulation & Abnormal Trading Detection: Build high-concurrency, low-latency real-time monitoring and interception models for wash trading, spoofing, pump & dump, front-running, and other illicit trading behaviors.
- Real-Time Anti-Fraud & Hybrid Architecture: Develop and optimize real-time risk control pipelines combining traditional machine learning and large-model intent recognition to accurately intercept P2P scams and abnormal trading groups, reducing financial losses.
- Automated Risk Control Document Review: Construct multimodal review pipelines for automatic document parsing and cross-validation.
Note: We are hiring for two specialized roles—AI Risk Control Algorithm Engineer and AI Large-Model Infrastructure Algorithm Engineer. Contact us via Telegram for details.
Job Requirements
- Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, or related fields (Master's preferred) with 5+ years of risk control algorithm experience.
- Strong data sensitivity and ability to independently define problems and drive solutions.
- Proficiency in Python + SQL and experience with large-scale data processing (Hive/Spark).
- Solid ML foundation, including feature engineering and end-to-end model optimization.
- Familiarity with graph algorithms for fraud mining and group identification.
- Knowledge of sequence models for behavioral anomaly detection.
- Experience with real-time systems (Flink/Kafka) and online inference pipeline design.
- Understanding of at least one of the following domains and their adversarial dynamics:
- Fraud countermeasures (e.g., bonus abuse, fake accounts, bulk attacks)
- Trading surveillance (e.g., wash trading, spoofing, pump & dump)
- P2P anti-scam/AML compliance
Preferred Qualifications:
- Experience in on-chain address analysis and fund tracing (e.g., Chainalysis/TRM).
- LLM applications in risk control (intent recognition, multimodal review, RAG QA, workflow orchestration).
- Design experience with risk rule engines or strategy platforms.
- Knowledge of orderbook mechanisms and market microstructure.
- Risk control background at top-tier exchanges or large fintech platforms.
- Publications in top conferences (KDD/AAAI/WWW).
Benefits
Negotiable
