Meta·Data Scientist·Technical Phone Screen·Senior

Senior

May 2026

Summary

Had a technical screen for a Data Scientist role at Meta focused on their internal bot-detection system called DOT. The whole session was basically one meaty scenario question about how you'd approach identifying malicious duplicate content at scale.

Questions Asked (1)

You're working on a bot-detection tool that needs to catch malicious duplicated content. What models or algorithms would you consider, and what's your reasoning for each?

Algorithms & Data StructuresTechnical Trade-offsSystem Design

Author's notes

I started with text hashing since it's cheap and obvious for near-exact duplicates, then moved into TF-IDF cosine similarity for fuzzier matches.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem: define 'duplicated content' (exact vs. near-duplicate), scale, and latency requirements. Then propose a layered approach: fast exact-match methods first, followed by near-duplicate detection using hashing and similarity measures, and finally ML models for semantic similarity. Explain trade-offs for each and how they fit together in a production system.

Pro tip: Emphasize that no single model is perfect; a cascaded system with cheap filters first and expensive models only for ambiguous cases balances accuracy and cost. Also mention monitoring and feedback loops to adapt to evolving adversarial tactics.

1. Clarify requirements and constraints

Ask about the scale of data, latency requirements, and definition of 'duplicated content' (exact copies vs. paraphrased). This shows you understand the problem before jumping to solutions.

2. Propose exact-match methods

For exact duplicates, use cryptographic hashing (e.g., SHA-256) or checksums. These are fast and scalable but fail on minor modifications.

3. Introduce near-duplicate detection

Use locality-sensitive hashing (LSH) like MinHash or SimHash to detect near-duplicates efficiently. These reduce dimensionality and allow approximate similarity search.

4. Leverage ML for semantic similarity

For paraphrased or semantically similar content, use embeddings (e.g., from BERT or Sentence-BERT) and compute cosine similarity. Train a classifier if labeled data is available.

5. Design a cascaded system and discuss trade-offs

Combine methods: start with exact hashing, then LSH, then ML for ambiguous cases. Discuss trade-offs: precision vs. recall, latency vs. accuracy, and computational cost.

Key Points to Mention

Exact hashing (e.g., SHA-256) for exact duplicates
Locality-sensitive hashing (MinHash, SimHash) for near-duplicates
Embedding-based similarity (e.g., BERT, Sentence-BERT) for semantic duplicates
Trade-offs: precision/recall, latency, scalability, and computational cost
Cascaded architecture to optimize resource usage
Adversarial adaptation: monitoring and retraining to handle evolving bot tactics

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