About the role
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. Our Ethos We believe in an open and flat structure. You will be able to grow into
the role that most aligns with your goals. Our team members at all levels have the freedom to explore ideas and impact the roadmap and vision of our company.
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. 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. What you’ll do Architect 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 Collaborate closely with talented engineers, data scientist and compliance teams across Rain Work in a fast-paced environment on a rapidly growing product suite Solve complex problems at the intersection of ML systems, data, and reliability
