Machine Learning Engineer - Fraud Risk

Rain

8 days
New York, NY, Remote $170K – $240K • Offers Equity • Offers Bonus
Python Crypto PyTorch TensorFlow Fintech scikit-learn Machine Learning Engineering Regulatory Compliance Risk Management Remote Senior On-site Full time Payments Transaction Monitoring Fraud Detection Fraud Prevention Fraud Banking SLA Management Model Training Generative AI Low-latency Systems ML Systems Anomaly Detection Behavioral Analysis ML Pipelines Data Ingestion Feature Engineering Model Deployment Continuous Monitoring Real-time Decision Systems ML Infrastructure Model Versioning Automated Retraining Shadow Deployment Rollback Deployment Model Performance Monitoring Latency Monitoring Data Quality Monitoring Drift Monitoring Model Explainability Drift Detection Adversarial Robustness ML Development Lifecycle Supervised Learning Unsupervised Learning Statistical Modeling Predictive Modeling Real-time Fraud Detection Offline Fraud Detection Graph-based Fraud Detection Network-level Fraud Detection Large Language Models Pattern Generation Synthetic Data Augmentation Model Governance Bias Mitigation
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Rain is a global stablecoin payments platform for enterprises, neobanks, platforms, developers, and AI agents. Our technology allows partners to move, store, and use stablecoins instantly and compliantly through global payment cards, rewards, on/offramps, wallets, and cross-border rails. As both a Visa and Mastercard Principal Member, Rain issues cards that work at more than 175 million merchant locations in over 220 countries and territories. Built natively for stablecoins and trusted by more than 100 organizations worldwide, Rain delivers secure, scalable infrastructure that makes money move freely and instantly around the world. In January 2026, Rain closed a $250M Series C led by ICONIQ, valuing the company at $1.95B, with Sapphire Ventures, Dragonfly, Bessemer Venture Partners, Galaxy Ventures, FirstMark, Lightspeed, Norwest, and Endeavor Catalyst also participating. The fraud risk management team at Rain creates sophisticated, scalable risk mitigation solutions to protect customers and deliver a low-friction experience. This is achieved by maintaining transaction and lifecycle event monitoring, building alerts to speed fraud detection and response, and creating risk rules and strategies powered by ML models. This team is a pillar of the business, supporting new products and ensuring their success. Rain’s next-generation payment technology introduces new fraud vectors that require holistic, end-to-end thinking, strong data fundamentals, and fraud management savvy to combat. In this role, you will architect and build scalable ML systems for fraud detection, anomaly detection, and behavioral analysis. You will develop and maintain end-to-end ML pipelines including data ingestion, feature engineering, model training, deployment, and continuous monitoring. You will design and implement low-latency, real-time decision systems partnering with fraud risk data scientists, integrating with transaction or behavioral data streams. Responsibilities also include owning ML infrastructure (model versioning, automated retraining, safe deployment strategies like shadow and rollback), building robust monitoring and alerting for model performance, latency, data quality, and drift. You will lead experimentation on model explainability, drift detection, and adversarial robustness for fraud prevention use cases, and develop tooling and processes to improve the effectiveness and speed of the ML development lifecycle. You will partner with platform teams to meet strict SLAs for availability, latency, and accuracy, collaborate closely with talented engineers, data scientists, and compliance teams across Rain, work in a fast-paced environment on a rapidly growing product suite, and solve complex problems at the intersection of ML systems, data, and reliability.