Google·Software Engineer·Onsite - System Design / Architecture
- Design an end-to-end fraud detection machine learning system for financial transactions.
- How would you handle the severe class imbalance between fraudulent and legitimate transactions in your model?
- Fraud labels often arrive days later via chargebacks. How does that affect your training pipeline and what do you do about it?
- How would you serve fraud predictions at low latency in a production environment, and how would you test changes safely?
- A new user or merchant has no transaction history. How does your system handle fraud detection for them?
“I started with problem framing which felt right, binary classification on transactions with extreme class imbalance.”