ML Engineer — Fraud Detection & Real-Time Systems

Rain; New York (NY); Hybrid; USD 170,000 - 240,000; Full time

Benefits offered by this job

Unlimited time off

Flexible working

Health and wellness benefits

401(k) with company match

Equity plan

Office meals and team summits

Job summary

Rain, the global stablecoin payments platform, seeks a seasoned ML engineer to architect fraud-detection systems and real-time decisioning. You will own end-to-end ML pipelines, collaborate with data scientists and engineers, and drive scalable risk solutions across a fast-growing product suite.

Responsibilities include building low-latency models, maintaining ML infrastructure, and ensuring robust monitoring and explainability for fraud prevention use cases.

Qualifications

• 5+ years of experience building ML systems in production with 2+ years in fraud/risk/anomaly domains.
• Degree in CS/Engineering/Statistics or related field.
• Proven track record designing and maintaining ML models at scale.

Responsibilities

• Architect and build scalable ML systems for fraud detection and anomaly detection.
• Develop end-to-end ML pipelines: data ingestion, feature engineering, model training, deployment, monitoring.
• Design low-latency real-time decision systems with data streams.
• Own ML infrastructure: versioning, retraining, safe deployment.
• Build monitoring and alerting for model performance, latency, data quality.
• Lead experiments on explainability, drift detection, and robustness.
• Develop tooling to speed ML development lifecycle.
• Collaborate with platform teams to meet SLAs.

Skills

Python

PyTorch

TensorFlow

scikit-learn

Fraud risk

Anomaly detection

ML systems

Statistical modeling

Education

CS/Engineering/Statistics

Tools

ML pipelines

Model deployment tooling

Job description

Rain, the global stablecoin payments platform, seeks a seasoned ML engineer to architect fraud-detection systems and real-time decisioning. You will own end-to-end ML pipelines, collaborate with data scientists and engineers, and drive scalable risk solutions across a fast-growing product suite.

Responsibilities include building low-latency models, maintaining ML infrastructure, and ensuring robust monitoring and explainability for fraud prevention use cases.