Google·Machine Learning Engineer·Onsite - System Design / Architecture
May 2026
Google ML Engineer system design round, one meaty question about video deduplication at scale. The whole session was basically a whiteboard deep-dive into embeddings, ANN search, and where you cut corners without killing accuracy. Felt like I held my own but the tradeoffs section got uncomfortable fast.
- Design a large-scale system to detect and remove near-duplicate short videos, handling millions of uploads per day.
- How would you handle the tradeoff between embedding dimensionality and search latency in this system?
- What approximate nearest neighbor strategies would you consider, and what are the tradeoffs between them?
- How would you ensure the system remains accurate over time as new video styles and encoding formats emerge?
“I started with feature extraction using a vision transformer and that part went fine.” The rest of the author's notes on Machine Learning Engineer interview at Google, Onsite - System Design / Architecture round, covers how they worked through the question, what the panel pushed back on, and what they would do differently.
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