← Openai Interview Insights

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

Senior
May 2026

Summary

System design round at OpenAI for an ML Engineer role, focused entirely on near-duplicate media detection at scale. The problem was meaty and I felt like I was mostly keeping my head above water the whole time.

Questions Asked (1)

Q1

Design a system to detect near-duplicate images and videos at massive scale, including reuploads, minor edits, re-encodes, and watermarked versions.

System DesignTechnical Trade-offsAlgorithms & Data Structures
Author's notes

This is basically the whole interview in one question.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale, then propose a two-stage pipeline: a fast approximate nearest neighbor search over compact embeddings to shortlist candidates, followed by a more precise verification step. Discuss trade-offs between recall, precision, latency, and cost, and how to handle both images and videos uniformly.

Pro tip: Emphasize the importance of a robust embedding model that is invariant to common transformations (resizing, compression, watermarks) and the need for a scalable ANN index like FAISS or HNSW with sharding and replication.

1. Clarify Requirements and Scale

Ask about data volume, query throughput, latency requirements, and definition of near-duplicate. Understand if the system is for offline batch processing or real-time detection.

2. Design Feature Extraction

Propose using deep learning models (e.g., CNNs or transformers) to generate compact embeddings for images and video frames. For videos, consider temporal aggregation or keyframe extraction.

3. Build Approximate Nearest Neighbor Index

Use an ANN library (e.g., FAISS, ScaNN, HNSW) to index embeddings for fast similarity search. Discuss sharding, quantization, and distributed serving for massive scale.

4. Implement Verification and Post-Processing

For shortlisted candidates, apply a more expensive verification (e.g., local feature matching, perceptual hashing, or a second-stage model) to reduce false positives. Handle watermarks and minor edits.

5. Address System Trade-offs and Operations

Discuss trade-offs between recall and precision, latency vs. cost, and how to update the index incrementally. Cover monitoring, evaluation metrics, and handling of adversarial cases.

Key Points to Mention

  • Choice of embedding model and its invariance properties (e.g., SimCLR, CLIP, or specialized models for near-duplicate detection).
  • Use of approximate nearest neighbor search (FAISS, HNSW) with quantization (PQ, OPQ) for memory efficiency.
  • Handling videos: keyframe extraction, temporal hashing, or 3D CNNs to capture spatiotemporal features.
  • Perceptual hashing (pHash, dHash) as a lightweight baseline and its limitations.
  • Scalability considerations: distributed indexing, sharding, and caching.
  • Evaluation metrics: precision@k, recall, and how to create a labeled dataset for near-duplicates.

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