Machine Learning Engineer - Fraud Risk
Tasks
- Architect and build scalable ML systems for fraud detection
- Build monitoring and alerting for model performance latency data quality and drift
- Collaborate with cross-functional teams to deploy ML solutions
- Design and implement real time decision systems
- Develop and maintain end-to-end ML pipelines
- Develop tooling and processes for ML development lifecycle
- Implement safe model deployment strategies
- Lead experimentation on model explainability drift detection and adversarial robustness
- Own ML infrastructure and model versioning
- Partner with platform teams to meet SLAs
Perks/Benefits
- 401k match
- Equity option plan
- Flexible working
- Health and wellness spending
- Health, dental, and vision plans
- Life insurance
- Team offsites
- Unlimited time off
- Work from home stipend
Skills/Tech-stack
Adversarial Robustness | Adversarial Testing | Anomaly Detection | Automated retraining | Data Quality | Data quality monitoring | Feature Engineering | Low Latency | Low-Latency Systems | Machine Learning | Model Deployment | Model Drift | Model Explainability | Model Monitoring | Model Training | Model versioning | PyTorch | Python | Quality monitoring | Real Time | Real-time Systems | Rollback | Scikit-learn | Shadow Deployment | Statistical modeling | Supervised Learning | TensorFlow | Time Systems | Unsupervised Learning
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
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