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Join Rain as a Machine Learning Engineer to build scalable ML systems for fraud detection and anomaly detection. Work in a fast-paced fintech environment with a competitive salary and equity options.
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.
Responsibilities
- Architect and build scalable ML systems for fraud detection, anomaly detection, and behavioral analysis.
- Develop and maintain end-to-end ML pipelines including data ingestion, feature engineering, model training, deployment, and continuous monitoring.
- Design and implement low-latency, real-time decision systems.
- Own ML infrastructure (model versioning, automated retraining, safe deployment strategies).
- 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.
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.
- Proven track record designing and maintaining ML models at scale.
- Advanced proficiency in Python and ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn).
- Strong understanding of supervised/unsupervised learning, anomaly detection, and statistical modeling.
- Ability to work autonomously, manage ambiguity, and collaborate closely with data scientists.
- Experience developing, validating, and productionalizing predictive real-time and offline fraud detection models.
- Experience collaborating with cross-functional teams to prioritize, scope, and deploy MLI solutions at scale.
- Domain expertise in banking, payments, or transaction monitoring (nice to have).
- Experience with graph-based or network-level fraud detection techniques (nice to have).
- A graduate degree in Computer Science, Engineering, Statistics, Applied Math, or a related technical field (nice to have).
- Experience fine-tuning or adapting generative AI / large language models (nice to have).
- Knowledge of model governance, bias mitigation, and regulatory compliance in fraud contexts (nice to have).
What We Offer
- Competitive salary: $170K – $240K.
- Offers equity.
- Offers bonus.
- Work in a fast-paced environment on a rapidly growing product suite.