Machine Learning Engineer - Fraud Risk

Skills

About the Role

You will design, build, and operate production ML systems that detect and prevent fraud. You will develop end-to-end pipelines from data ingestion and feature engineering to model training, deployment, and continuous monitoring. You will implement low-latency decision systems, build monitoring and alerting for model performance and drift, and collaborate with engineers, data scientists, and compliance to ship reliable fraud prevention solutions.

Requirements

• 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 learning, 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 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 integrating 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 closely with engineers, data scientists, and compliance teams

Benefits

• Unlimited time off (minimum 10 days required)
• Flexible working (remote or office)
• Home office stipend
• Comprehensive health, dental, and vision plans
• 100% company subsidized life insurance
• 401(k) with 4% company match
• Equity option plan
• Bonus
• Rain Cards for product testing
• Health and wellness spending allowance
• Team and company off-sites (domestic and international)