Machine Learning Engineer - Fraud Risk

About The Company

Rain makes the next generation of payments possible across the globe. We’re a lean and mighty team of passionate builders and veteran founders. Our infrastructure makes stablecoins usable in the real-world by powering card transactions, cross-border payments, B2B purchases, remittances, and more. We partner with fintechs, neobanks, and institutions to help them launch solutions that are global, inclusive, and efficient. You will have the opportunity to deliver massive impact at a hypergrowth company that is funded by some of the top investors in fintech, crypto, and SaaS, including Sapphire Ventures, Norwest, Galaxy Ventures, Lightspeed, Khosla, and several more. If you’re curious, bold, and excited to help shape a borderless financial future, we’d love to talk.

About The Team

The fraud risk management team at Rain creates sophisticated, scalable risk mitigation solutions to protect our customers and deliver a low-friction experience. We achieve this 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. We are a pillar of the business, supporting new products and ensuring their success.

What you’ll do

• Arcitect and build scalable ML systems for fraud detection, anomaly detection, and behavioral analysis
• Develop and maintain end-to-end ML pipelines: data ingestion, feature engineering, model training, deployment, and continuous monitoring
• Design and implement low-latency, real-time decision systems partnering with fraud risk data scientists, integrating with transaction or behavioral data streams
• Own ML infrastructure, including model versioning, automated retraining, and safe deployment strategies (e.g., shadow, rollback)
• Build robust monitoring and alerting for model performance, latency, data quality, and drift
• Lead experimentation on model explainability, drift detection, and adversarial robustness for fraud prevention use cases
• Develop tooling and processes to improve the effectiveness and speed of the ML development lifecycle
• Partner with platform teams to meet strict SLAs for availability, latency, and accuracy
• Solve complex problems at the intersection of ML systems, data, and reliability

Qualifications

• 5+ years of experience building ML systems in production; at least 2+ in fraud, risk, or anomaly detection domains
• A degree in Computer Science, Engineering, Statistics, Applied Math, or a related technical field
• Advanced proficiency in Python and ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn)
• Strong understanding of supervised/unsupervised learning, anomaly detection, and statistical modeling
• Experience developing, validating, and productionalizing predictive real-time and offline fraud detection models using supervised and unsupervised ML techniques
• Experience collaborating with cross-functional teams to prioritize, scope, and deploy MLI solutions at scale

Benefits

• Unlimited time off
• Flexible working
• Easy to access benefits
• Compensation Range: $50K - $999K