DoubleVerify·Software Engineer·Onsite - Product Sense / Strategy·Senior

Senior

Apr 2026

Summary

Interview at DoubleVerify for what seemed like a product or ML-adjacent role. Single design question, pretty open-ended, felt more like a whiteboard session than a structured interview.

Questions Asked (1)

How would you design a webpage content classification engine? Walk through the logic, the KPIs you'd track, and any assumptions you're making.

System DesignProduct Analytics & MetricsProduct Strategy

Author's notes

This is the kind of question that sounds manageable until you realize how many directions it can go.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and assumptions, then outline a high-level architecture for the classification engine, covering data ingestion, feature extraction, model training, and serving. Discuss the trade-offs between different approaches (e.g., rule-based vs. ML) and how you would measure success with KPIs like accuracy, latency, and throughput.

Pro tip: Emphasize the importance of a feedback loop: how you would collect labeled data from user interactions or manual reviews to continuously improve the model, and how you'd handle concept drift over time.

1. Clarify Requirements and Assumptions

Ask questions to understand the scale (pages per second), content types (text, images, video), classification categories (e.g., topics, sentiment, safety), and latency requirements. State assumptions explicitly.

2. High-Level Architecture

Propose a pipeline: content ingestion (crawling or API), preprocessing (cleaning, tokenization), feature extraction (TF-IDF, embeddings), model inference (e.g., fine-tuned BERT or lightweight CNN), and post-processing (thresholding, aggregation).

3. Model Selection and Training

Discuss model choices: start with a baseline (logistic regression) then move to deep learning if needed. Cover training data collection, labeling, and evaluation metrics (precision, recall, F1).

4. Deployment and Scaling

Explain how to serve the model: batch vs. real-time, use of model serving frameworks (TensorFlow Serving, TorchServe), caching, and horizontal scaling. Address latency and throughput trade-offs.

5. Monitoring and KPIs

Define KPIs: model performance (accuracy, F1), operational (latency, throughput, error rates), and business (e.g., reduction in manual review). Describe monitoring and alerting, and a feedback loop for retraining.

Key Points to Mention

Trade-offs between rule-based and ML approaches, and when to use each.
Handling imbalanced classes and multi-label classification.
Feature engineering techniques for text (e.g., TF-IDF, word embeddings, contextual embeddings).
Model evaluation metrics beyond accuracy: precision, recall, F1, AUC-ROC.
Scalability considerations: distributed inference, batching, and caching.
Continuous learning: active learning, human-in-the-loop, and concept drift detection.

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