About the Team

About the Role

• Identity trust & risk modeling — fusing verification scores, device, geo, and behavioral data into identity-quality and trust signals.
• Cluster & relationship intelligence — graph and clustering methods to identify account farms, mass registration, and coordinated attacks.
• Behavioral risk modeling — behavioral features and sequence models for account takeover and abnormal-session detection.
• Biometric & multimodal signals — face embeddings, liveness/anti-spoofing, DeepFake and injection defense, document forgery and AIGC detection, used as inputs to identity trust and the risk layer.
• AI agents — applying agentic AI to scale investigation, detection, and operational defense.

What You'll Be Doing

• Build identity trust & risk models: Design and ship coarse-to-fine risk models that fuse face/liveness/document verification scores, device, IP and geo signals, and behavioral data, for high-concurrency real-time decisioning.
• Uncover organized fraud with graph & clustering: Build entity graphs across accounts, devices, faces, documents and templates, and apply community detection, clustering, and GNN methods to detect fraud rings and large-scale abuse.
• Develop behavioral features & sequence models: Engineer real-time and offline behavioral features, and build sequence models to detect account takeover, bot/script behavior, and abnormal sessions.
• Leverage biometric & multimodal signals: Turn biometric features (face embeddings, face quality, liveness and anti-spoofing results) and document-forgery and DeepFake signals into reliable identity-trust inputs for risk decisions, including biometric similarity for cross-account linkage and duplicate-identity detection. Work with the face, liveness, and document teams, or build models yourself.
• Drive execution & collaboration: Deliver measurable results while managing ambiguity; partner with backend, product, infrastructure, risk, and compliance teams to integrate models into the production decision chain. Own metric definitions, the balance between false positives and missed fraud, pass rate, monitoring, and drift handling.
• Advance applied R&D: Experiment with graph learning, multimodal fusion, and AI agents; stay current with academic and industry advances and translate them into practical defenses.
• Raise the technical bar: Contribute to design reviews, share applied-AI best practices, and mentor junior engineers.

What We Look For

• 5+ years of hands-on experience building risk-control, fraud-detection, or identity-trust models, including significant experience deploying ML systems into production.
• Graph & clustering expertise: Experience applying graph mining, community detection, clustering, or GNN methods to fraud or abuse detection.
• Behavioral modeling: Solid experience with behavioral feature engineering and sequence modeling, including real-time feature pipelines.
• Deep AI/ML expertise: Strong proficiency with PyTorch and/or TensorFlow, and solid experience with production ML systems.
• Strong technical & coding skills: Strong coding ability (e.g., Python, SQL) and the system-level thinking to design end-to-end solutions with high availability and low latency.
• Adversarial mindset: Understanding of how fraudsters adapt, and how to evaluate and iterate defenses against evolving attacks.
• Collaboration: Strong communication skills, with experience working across engineering, product, and risk teams.
• Education: Bachelor's degree or higher in Computer Science, Electrical Engineering, or a related field.

Preferred Qualifications

• Experience in identity verification, KYC/KYB, AML, face-payment or payment risk control, or biometric security.
• Hands-on experience with face recognition, liveness/anti-spoofing, OCR, document parsing, fake-ID detection, or media forensics.
• Experience with graph databases or graph computing (e.g., Neo4j, GraphX) and real-time graph features.
• Knowledge of data and AI/ML infrastructure (e.g., Spark, Flink, Kafka, Airflow, Snowflake).
• Familiarity with MLOps best practices: monitoring, retraining, and continuous improvement of production models.
• Experience with LLMs and AI agents.
• Experience working in fast-paced technology companies or security-critical environments.
• Research publications, patents, or open-source contributions in relevant AI fields.