Apple·Machine Learning Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

Apr 2026

Summary

Apple ML engineer interview, one technical question about implementing batch normalization from scratch with NumPy. Pretty focused session, no fluff.

Questions Asked (1)

Can you implement batch normalization from scratch using NumPy?

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I knew the formula conceptually but fumbled the epsilon term placement under the square root.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the scope: training vs. inference, and whether to include backpropagation. Then implement the forward pass for training (compute batch mean and variance, normalize, scale and shift) and inference (use running averages), and optionally the backward pass. Finally, discuss trade-offs and edge cases.

Pro tip: Mention that in inference, you use the running mean and variance computed during training, not the current batch statistics. Also, highlight the importance of numerical stability by adding a small epsilon to the variance.

1. Clarify requirements

Ask whether the implementation should include both forward and backward passes, and whether it's for training or inference. Confirm if they want a complete layer or just the normalization function.

2. Implement forward pass for training

Compute batch mean and variance along the feature dimension. Normalize inputs using these statistics, then apply learnable scale (gamma) and shift (beta) parameters.

3. Implement forward pass for inference

Use running averages of mean and variance (computed during training) to normalize inputs. This ensures deterministic outputs at test time.

4. Implement backward pass (if required)

Derive gradients with respect to inputs, gamma, and beta. Use the chain rule and account for the batch statistics' dependence on inputs.

5. Discuss trade-offs and edge cases

Talk about computational cost, memory usage, and behavior with small batch sizes. Mention alternatives like layer normalization and why batch norm may not suit recurrent networks.

Key Points to Mention

Batch normalization normalizes each feature independently across the batch.
Learnable parameters gamma and beta allow the network to undo normalization if needed.
During training, use batch statistics; during inference, use running averages.
Numerical stability: add epsilon to variance before sqrt.
Backward pass requires computing gradients through mean and variance.
Batch norm reduces internal covariate shift and acts as a regularizer.

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