Microsoft·Software Engineer·Technical Phone Screen·Senior

Senior

Apr 2026

Summary

Applied Scientist interview at Microsoft that went deep on CTR prediction, basically a full system design walkthrough covering everything from raw data to live A/B tests. Dense question, lots of ground to cover.

Questions Asked (1)

Walk through a complete CTR prediction system end-to-end: how you'd define labels, engineer features, choose a model, handle imbalance and bias, calibrate outputs, evaluate offline, and deploy with A/B testing.

System DesignA/B Testing & ExperimentationTechnical Trade-offs

Author's notes

This was basically a marathon question.

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AI HintsAI Generated

Suggested Approach

Structure your answer as a lifecycle narrative, starting from problem framing and data/label definition, then moving through feature engineering, model selection, training with imbalance/bias handling, calibration, offline evaluation, and finally deployment with A/B testing. Emphasize trade-offs and Microsoft-specific considerations like scalability, fairness, and integration with existing systems.

Pro tip: Show awareness of production constraints by mentioning how you'd monitor for data drift and feedback loops, and how you'd iterate based on A/B test results—this demonstrates end-to-end ownership beyond just modeling.

1. Define Problem and Labels

Clarify the prediction goal (e.g., click probability) and define labels from user interactions, handling delayed feedback and negative sampling. Discuss how to avoid label leakage and ensure consistency across training and serving.

2. Engineer Features and Handle Data Issues

Design features from user, item, context, and interaction history, including real-time and batch features. Address class imbalance via resampling or weighting, and mitigate bias through techniques like reweighting or adversarial debiasing.

3. Select and Train Model

Choose a model balancing accuracy and latency (e.g., logistic regression for baseline, then GBDT or deep learning for scale). Train with proper validation, using techniques like negative sampling and regularization to prevent overfitting.

4. Calibrate and Evaluate Offline

Calibrate predicted probabilities using Platt scaling or isotonic regression to align with true click rates. Evaluate with metrics like AUC, log loss, and calibration plots, and simulate online metrics via replay or counterfactual evaluation.

5. Deploy and A/B Test

Deploy model with a serving infrastructure that supports low-latency inference and fallback. Design A/B tests with proper randomization, sample size, and guardrail metrics; monitor for drift and iterate based on results.

Key Points to Mention

Label definition: using clicks as positive labels, handling delayed feedback and negative sampling strategies.
Feature engineering: incorporating user demographics, item attributes, context (time, device), and historical interactions; real-time vs batch features.
Imbalance and bias: techniques like downsampling negatives, upweighting positives, and fairness constraints to avoid demographic bias.
Model choice: trade-offs between logistic regression, GBDT, and deep learning; considering latency and scalability.
Calibration: importance of calibrated probabilities for downstream bidding or ranking; methods like Platt scaling and isotonic regression.
Offline evaluation: using AUC, log loss, calibration curves, and counterfactual evaluation; avoiding leakage and ensuring temporal validation.
Deployment and A/B testing: infrastructure for low-latency serving, A/B test design with proper metrics, and monitoring for drift and feedback loops.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.