Staff Applied Machine Learning Engineer - Fraud & Abuse
Bay Area, CA, United States of America
USD 276K-415K Senior-level Full Time
Tasks
- Build and operate real time and batch risk models
- Conduct incident response for ML systems
- Create reusable decision and evaluation capabilities
- Deploy models with safe rollout and rollback
- Design production ML decision systems
- Develop feedback loops for triage and investigations
- Ensure online offline consistency
- Implement low latency inference and decision APIs
- Integrate behavioral graph device network and event signals
- Manage data contracts and feature quality
- Monitor models drift and performance
- Simulate controls and improve post incident learning
- Support alert clustering and graph exploration
Perks/Benefits
Skills/Tech-stack
Anomaly Detection | Batch Processing | Docker | Drift Detection | Embeddings | Entity Resolution | Evaluation | Event Streaming | Feature Store | Graph Machine Learning | Java | Kafka | Kotlin | Kubernetes | LightGBM | Low Latency | Low Latency Inference | Machine Learning | Machine learning modeling | Model Deployment | Model Monitoring | Observability | PyTorch | Python | SQL | TensorFlow | XGBoost
Education
N/A
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