Machine Learning Engineer — Risk & AI

Building machine learning systems for risk at a global crypto exchange is fundamentally different from conventional ML engineering. The data spans on-chain activity, fiat deposits and withdrawals, trading behaviour, account access patterns, device intelligence, identity information, and customer interactions—signals that very few organizations can analyze together.

The problems are complex, adversarial, and constantly evolving. Models must identify emerging fraud patterns, scams, account takeovers, payment abuse, and other forms of financial risk while minimizing disruption to legitimate customers. Success is not measured only through offline model metrics. It is measured through prevented losses, improved approval rates, reduced false positives, faster investigations, and more reliable customer experience.

This role sits within a multidisciplinary risk team of machine learning engineers, data scientists, risk strategy specialists, analytics engineers, product managers, and operations teams. You will work across the full ML lifecycle — from problem formulation, feature engineering, and model development to real-time deployment, monitoring, experimentation, and continuous iteration.