Machine Learning Engineer - Fraud Risk

Rain; United States; Remote; USD 140,000 - 190,000; Full time

Job summary

Rain is seeking a senior Machine Learning Engineer to develop scalable systems for fraud and anomaly detection. You will architect and build end-to-end ML pipelines, ensuring models are trained, deployed, and monitored in production.

Within the fraud risk management context, you will design low-latency decision systems, emphasize model explainability, and collaborate with cross-functional teams to meet SLAs and improve the ML development lifecycle.

Qualifications

• Extensive experience in ML production environments, with a focus on fraud detection.
• Strong Python skills and experience with ML frameworks.
• Ability to design scalable ML systems and end-to-end pipelines.

Responsibilities

• Architect and build end-to-end ML pipelines for fraud and anomaly detection.
• Develop scalable, low-latency ML models deployed in production.
• Collaborate with cross-functional teams to meet SLAs and improve ML lifecycle.

Skills

Python

ML frameworks

Job description

Rain seeks a senior Machine Learning Engineer to develop scalable systems for fraud and anomaly detection. Ideal candidates have extensive experience in ML production environments, particularly in fraud detection. Strong Python and ML framework skills are essential.

In this role, you'll architect and build scalable machine learning systems focused on fraud detection and anomaly detection. You'll be responsible for developing end-to-end ML pipelines, ensuring that models are effectively trained, deployed, and monitored in a production environment.

Working within the fraud risk management team, you'll design low-latency decision systems and lead efforts in model explainability and robustness. Collaboration with cross-functional teams will be crucial to meet service level agreements and enhance the ML development lifecycle.