Machine Learning Engineer - Fraud Risk
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)