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

SeniorPrefer not to say

Jun 2026

Summary

Google system design interview for what felt like an ML-adjacent role. One question, image classification with some real-world constraints baked in. Not a pure coding round but not a pure ML theory round either, somewhere in the middle.

Questions Asked (1)

Design a system that can automatically identify images of bald eagles captured in national parks.

System DesignTechnical Trade-offsAdaptability & Ambiguity

Author's notes

I started with data collection and spent way too long on it.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem scope and requirements, then outline a high-level ML system design covering data collection, model training, and deployment. Emphasize trade-offs between accuracy, latency, and cost, and discuss how to handle ambiguity in real-world settings.

Pro tip: Demonstrate awareness of Google's ML infrastructure (e.g., Vertex AI, TFX) and propose a phased approach: start with a simple baseline, then iterate with more complex models and active learning.

1. Clarify Requirements

Ask questions to understand the scale, latency, accuracy needs, and data availability. Define what 'identify' means: classification, detection, or both?

2. Data Strategy

Discuss data collection from national parks, labeling (e.g., using citizen science or expert annotators), and handling class imbalance and domain shift.

3. Model Selection & Training

Propose a two-stage approach: object detection (e.g., EfficientDet) to localize birds, then classification (e.g., EfficientNet) to identify bald eagles. Mention transfer learning and data augmentation.

4. Evaluation & Iteration

Define metrics (precision, recall, F1, mAP) and set up a feedback loop for continuous improvement. Discuss handling false positives (other birds) and false negatives.

5. Deployment & Monitoring

Outline serving infrastructure (batch vs. real-time), model versioning, and monitoring for drift. Consider edge deployment for remote parks with limited connectivity.

Key Points to Mention

Transfer learning from pre-trained models on similar tasks (e.g., iNaturalist)
Handling class imbalance and rare species detection
Trade-offs between model complexity, latency, and cost
Use of active learning to improve model with minimal labeling
Integration with existing park systems and privacy considerations
Scalability and monitoring for model drift in diverse environments

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