Job Description
Mission: Leverage hybrid architecture to comprehensively enhance the exchange's risk defense and automated decision-making capabilities, and participate in building the next-generation "AI-Ready" risk control system.
- Black Market Investigation & Graph Analysis: Utilize graph neural networks (GNN) and other algorithms to mine and monitor online fraudulent activities and high-risk address networks by analyzing massive transaction 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 proactively.
- Market Manipulation & Abnormal Trading Detection: Build high-concurrency, low-latency real-time monitoring and interception models for abnormal trading behaviors such as wash trading, spoofing, pump & dump, and front-running.
- Real-Time Anti-Fraud & Hybrid Architecture: Develop and optimize a real-time risk control pipeline combining traditional machine learning and large-model intent recognition to accurately intercept P2P fraud and suspicious trading groups, reducing financial losses.
- Automated Risk Control Material Review: Construct a multimodal review pipeline to achieve automatic document parsing and cross-validation.
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 transaction patterns and on-chain data to identify fraudulent activities.
- Develop and optimize AI-driven risk control models to enhance detection accuracy and efficiency.
- Collaborate with cross-functional teams to implement real-time monitoring systems for market manipulation.
- Design and refine automated review processes for risk control documentation.
- Stay updated with the latest advancements in AI and blockchain technology to improve risk mitigation strategies.
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 with strong problem-solving and implementation skills.
- 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 domains: fraud prevention (e.g., farming, bulk attacks), trading surveillance (e.g., wash trading, spoofing), or 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, workflow orchestration).
- Background in designing risk control rule engines or strategy platforms.
- Knowledge of order book mechanisms and market microstructure.
- Prior experience in risk control at top-tier exchanges or large fintech platforms.
- Publications in top conferences such as KDD, AAAI, or WWW.
Benefits
Negotiable
