Snapchat·Machine Learning Engineer·Onsite - System Design / Architecture·Senior

Senior

Jun 2026

Summary

Snapchat ML Engineer system design round, one massive question about building a harmful content detection system from scratch. The scope was enormous and I felt like I was constantly triaging what to cover versus what to skip.

Questions Asked (1)

Design a complete harmful content detection system covering taxonomy definition, labeling, multilingual and multimodal support, model choices, inference architecture, human review workflows, adversarial robustness, fairness, and continuous improvement.

System DesignTechnical Trade-offsA/B Testing & Experimentation

Author's notes

This was basically five system design questions rolled into one.

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AI HintsAI Generated

Suggested Approach

Structure your answer as a layered system: start with taxonomy and data, then model and inference architecture, then human-in-the-loop and governance, and finish with robustness, fairness, and continuous improvement. Emphasize trade-offs (latency vs. accuracy, precision vs. recall, automation vs. human review) and tie decisions to Snapchat's scale, multimodal content, and teen safety priorities.

Pro tip: Anchor every design choice to a concrete metric and feedback loop (e.g., precision@high-recall threshold, review SLA, adversarial recall, fairness gap), and explicitly discuss how you'd A/B test policy thresholds and model versions without harming user experience.

1. Define taxonomy and policy mapping

Start by defining a hierarchical, mutually exclusive taxonomy of harmful content (e.g., hate, harassment, self-harm, violence, sexual content, misinformation) mapped to enforcement actions and severity levels. Clarify how taxonomy supports multilingual and multimodal content and how it evolves with policy.

2. Data, labeling, and multimodal/multilingual pipeline

Describe data sourcing (user reports, proactive sampling, synthetic/adversarial examples), annotation guidelines, and quality control (gold sets, inter-annotator agreement). Explain how you handle multilingual text (language detection, translation, multilingual encoders) and multimodal inputs (image, video, audio, text) with separate or fused encoders.

3. Modeling and inference architecture

Propose a cascade: fast, cheap classifiers for high-volume filtering, followed by heavier multimodal models for ambiguous cases, with a final human review layer. Discuss model choices (e.g., fine-tuned transformers, CLIP-style multimodal models, graph-based propagation), latency/throughput constraints, and serving infrastructure (batch vs. real-time, edge vs. cloud).

4. Human review, adversarial robustness, and fairness

Design human-in-the-loop workflows with prioritization, reviewer well-being, and feedback to models. Address adversarial robustness via red-teaming, adversarial training, and anomaly detection. Ensure fairness by measuring performance across demographics, languages, and content types, and mitigating bias via data augmentation, reweighting, or fair representation learning.

5. Continuous improvement and experimentation

Set up monitoring for drift, false positive/negative rates, and reviewer agreement. Use A/B testing to evaluate model updates and policy thresholds, with guardrail metrics (user reports, appeals, retention). Close the loop with active learning, hard negative mining, and periodic taxonomy audits.

Key Points to Mention

Taxonomy design: hierarchical, severity-based, and mapped to enforcement actions; include edge cases like satire, education, and news.
Multilingual and multimodal handling: language detection, translation, multilingual embeddings, and fusion of text, image, video, and audio signals.
Model cascade and inference trade-offs: precision/recall, latency, cost, and use of lightweight vs. heavy models with human review for ambiguous cases.
Human review workflows: prioritization, reviewer training and well-being, quality assurance, and feedback loops to models.
Adversarial robustness: red-teaming, adversarial training, obfuscation detection, and continuous monitoring for evasion tactics.
Fairness and continuous improvement: bias measurement across groups, mitigation strategies, A/B testing with guardrail metrics, and active learning.

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