This is a beast of a question and I underestimated how much they'd want me to go wide before going deep.
Start by clarifying the scope and requirements, then walk through the system architecture in a logical flow from data ingestion to feedback loops. Emphasize trade-offs at each stage, such as precision vs. recall, latency vs. accuracy, and scalability considerations. Conclude by discussing how you would measure success and iterate.
Pro tip: Highlight the importance of human-in-the-loop for labeling and feedback, and discuss how to handle adversarial evasion tactics, as this shows depth in building robust systems at scale.
Ask questions to understand scale (e.g., number of ads per day), latency requirements, precision/recall targets, and integration points with existing systems. Define what constitutes a weapon sale ad and the legal/ethical constraints.
Outline how to ingest ads from various sources (text, images, videos) in real-time or batch. Discuss preprocessing steps like text normalization, image resizing, and handling missing modalities.
Describe feature extraction for each modality (e.g., text embeddings, object detection for images, audio transcription). Explain labeling strategies, including active learning and human review, to create a high-quality dataset.
Propose a multimodal model architecture (e.g., late fusion or early fusion) that combines features. Discuss training considerations like class imbalance, data augmentation, and evaluation metrics.
Design the inference pipeline for low-latency predictions, including model serving, caching, and fallback mechanisms. Explain how to collect user feedback and model predictions to continuously retrain and improve the system.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was the part that actually tripped me up.
Start by clarifying the problem scope and constraints, then propose a multi-layered detection system that combines anomaly detection, few-shot learning, and human-in-the-loop verification. Emphasize the need for a feedback loop to continuously update the model and adapt to new threats.
Pro tip: Acknowledge that no system can perfectly detect unknown unknowns, so focus on designing a robust pipeline that minimizes false negatives and enables rapid iteration. Show awareness of Meta's scale and the importance of balancing precision and recall in a production environment.
Ask questions to understand the scale, latency requirements, available data, and acceptable false positive/negative rates. This ensures your solution is tailored to the specific context.
Propose a system that first uses unsupervised anomaly detection to flag potential novel weapons, then applies few-shot learning or meta-learning to classify them with minimal examples.
Route uncertain detections to human reviewers, and use their feedback to retrain the model, enabling continuous improvement and adaptation to new weapon categories.
Discuss trade-offs between model complexity, inference speed, and accuracy. Propose a scalable architecture using distributed processing and model versioning to handle Meta's scale.
Outline metrics like precision, recall, and time-to-detection for novel categories, and set up monitoring to detect drift and trigger retraining.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.