The 60-second answer
Define the moderation policy first: taxonomy, severity, latency, human-review/appeal path, and false-positive vs false-negative costs. Build policy-consistent labels with hard negatives; use staged filtering/classification with calibrated confidence and human review for uncertain or high-impact cases.
Build the answer in this order
Define the moderation policy first: taxonomy, severity, latency, human-review/appeal path, and false-positive vs false-negative costs.
Build policy-consistent labels with hard negatives; use staged filtering/classification with calibrated confidence and human review for uncertain or high-impact cases.
Evaluate class-wise precision/recall, calibration, policy-weighted cost, and important safety slices—not a single aggregate accuracy number.
Monitor prevalence, reviewer disagreement, appeal reversals, drift, latency, adversarial adaptation, and feedback contamination.
A useful interview mental model
This is the shape of a strong answer—not a script to memorize.
Senior-level signal
- Separate policy correctness from model accuracy and version them independently.
- Design for adversarial users, delayed ground truth, reviewer quality, and safe rollback when enforcement rates shift.
What the interviewer is really testing
Likely follow-up questions
Common weak-answer patterns
- Jumping to a model before defining the product contract.
- Listing components without bottlenecks, metrics, or failure handling.
- Ignoring data quality, serving latency, monitoring, and iteration.