Amazon·Machine Learning Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Amazon ML engineer interview, came down to a technical phone screen focused on model fundamentals. Pretty standard stuff but the loss function question had more depth to it than I expected.

Questions Asked (1)

What are some effective loss functions used in computer vision models?

Technical Trade-offsAlgorithms & Data Structures

Author's notes

I started with cross-entropy for classification and MSE for regression tasks, then moved into stuff like focal loss for class imbalance and IoU-based losses for object detection.

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AI HintsAI Generated

Suggested Approach

Start by categorizing loss functions based on the type of computer vision task (classification, detection, segmentation, etc.) and then discuss specific examples within each category, highlighting their strengths and weaknesses. Emphasize how the choice of loss function depends on the task, data characteristics, and model architecture, and mention any Amazon-specific considerations like scalability and production deployment.

Pro tip: Demonstrate awareness of trade-offs by discussing not only standard losses but also recent advancements and how they address specific challenges like class imbalance or hard example mining. Relate your answer to real-world impact, such as how a loss function choice affected a project's performance metric.

1. Categorize by Task

Organize loss functions by computer vision tasks: classification, object detection, semantic segmentation, and generative tasks. This shows structured thinking and helps the interviewer follow your reasoning.

2. Discuss Common Losses per Task

For each category, name 1-2 widely used loss functions (e.g., Cross-Entropy for classification, Focal Loss for detection) and briefly explain how they work and why they are effective.

3. Highlight Trade-offs and Considerations

Explain when to choose one loss over another, considering factors like class imbalance, outlier sensitivity, and computational efficiency. Mention how loss functions interact with model architecture and optimization.

4. Connect to Amazon Scale and Production

Tie your answer to Amazon's context by discussing scalability, handling large datasets, and deployment constraints. For example, mention how loss functions like ArcFace or triplet loss are used in face recognition at scale.

5. Summarize with Best Practices

Conclude with best practices for selecting and tuning loss functions, such as starting with a standard loss and then experimenting with alternatives based on validation metrics.

Key Points to Mention

Cross-Entropy Loss and its variants (e.g., Binary Cross-Entropy, Categorical Cross-Entropy) for classification tasks.
Focal Loss for addressing class imbalance in object detection (e.g., RetinaNet).
Dice Loss and IoU Loss for semantic segmentation, especially with imbalanced classes.
Triplet Loss and Contrastive Loss for metric learning and face recognition.
Perceptual Loss and Adversarial Loss for generative models (e.g., GANs, style transfer).
Importance of loss function selection in model convergence, performance, and robustness.

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