Machine Learning Engineer - Fraud Risk
Rain helps users buy, sell, and swap cryptocurrencies with regulated custody, bank-grade security, and 24/7 support across web and mobile.
Funding history
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About Rain
Rain is a centralized crypto platform for retail and business users to buy, sell, and swap digital assets. The platform is licensed by the Central Bank of Bahrain and ADGM’s FSRA, offers cold storage custody, and provides bilingual 24/7 human support. Users can manage portfolios, use advanced trading with charts and order types, and access an OTC desk for large orders. The service includes clear fees, verification, and mobile apps.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
Design, build, and operate production machine learning systems that detect and prevent fraud, from data ingestion and feature engineering through model training, deployment, and continuous monitoring.
Requirements
- 5+ years of experience building ML systems in production, including 2+ years in fraud, risk, or anomaly detection
- Degree in Computer Science, Engineering, Statistics, Applied Math, or a related technical field
- Track record designing and maintaining ML models at scale
- Advanced proficiency in Python and ML frameworks such as PyTorch, TensorFlow, and scikit-learn
- Strong understanding of supervised learning, unsupervised learning, anomaly detection, and statistical modeling
- Ability to work autonomously, manage ambiguity, and collaborate with data scientists
- Experience productionalizing predictive real-time and offline fraud detection models
- Experience collaborating with cross-functional teams to deploy ML solutions at scale
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 monitoring
- Design and implement low-latency real-time decision systems using transaction and behavioral data streams
- Own ML infrastructure including model versioning, automated retraining, and safe deployment strategies
- Build monitoring and alerting for model performance, latency, data quality, and drift
- Lead experimentation on model explainability, drift detection, and adversarial robustness
- Develop tooling and processes to improve the ML development lifecycle
- Partner with platform teams to meet SLAs for availability, latency, and accuracy
- Collaborate with engineers, data scientists, and compliance teams
Benefits
- Unlimited time off with a 10-day minimum
- Flexible working from home or office
- Home office stipend
- Comprehensive health, dental, and vision plans
- Company-subsidized life insurance
- 401(k) with 4% company match
- Equity option plan
- Bonus
- Rain Cards for product testing
- Health and wellness spending allowance
- Domestic and international team off-sites