Job Description:
Mission: Leverage hybrid architectures to comprehensively enhance the risk defense and automated analysis capabilities of the exchange, and participate in building the next-generation "AI-Ready" risk control system.
- Black Market Mining & Graph Analysis: Utilize graph neural networks (GNN) and other algorithms to mine and monitor online fraud intelligence and high-risk address networks by analyzing massive trading behaviors and on-chain data.
- Risk Control Strategy Assistance & Risk Prediction: Use AI to assist in risk rule refinement and strategy optimization, automatically identify vulnerabilities in existing risk control rules, and develop forward-looking risk prediction models to prevent issues before they occur.
- Market Manipulation & 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 & Hybrid Architecture: Develop 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: Construct multimodal review pipelines to achieve automatic parsing and cross-validation of materials.
Note: Positions available for both AI Risk Control Algorithm Engineer and AI Large Model Infrastructure Algorithm Engineer. Contact via Telegram for details.
Key Responsibilities:
- Conduct in-depth analysis of trading behaviors and on-chain data to identify and mitigate risks.
- Collaborate with cross-functional teams to refine and implement risk control strategies.
- Develop and deploy real-time monitoring systems to detect and prevent fraudulent activities.
- Optimize existing risk control models and pipelines for better performance and accuracy.
- Stay updated with the latest advancements in AI and machine learning to enhance risk control capabilities.
Job Requirements:
- Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, or related fields (Master's preferred).
- 5+ years of experience in risk control algorithms.
- Strong data sensitivity and ability to independently define problems and drive solutions.
- Proficiency in Python and SQL, with experience in large-scale data processing (Hive/Spark).
- Solid foundation in machine learning, including feature engineering and model optimization.
- Familiarity with graph algorithms for fraud detection 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 areas: fraud prevention (scams/bulk attacks), trading monitoring (wash trading/spoofing), P2P anti-fraud, or AML compliance.
Preferred Qualifications:
- Experience in on-chain address analysis and fund tracing (e.g., Chainalysis, TRM).
- Practical experience with LLM in risk control (intent recognition, multimodal review, RAG, workflow orchestration).
- Design experience with risk control rule engines or strategy platforms.
- Knowledge 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
