Meta·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

Jun 2026

Summary

Meta ML engineer screen focused entirely on attention mechanics. One question, pretty deep, they wanted you to write it out by hand and then defend your implementation choices.

Questions Asked (1)

Implement the forward pass of single-head scaled dot-product attention by hand, including masking and numerically stable softmax. Then walk through time complexity and at least two edge cases.

Algorithms & Data StructuresTechnical Trade-offsSystem Design

Author's notes

This felt manageable until they asked about numerical stability and I had to actually justify the max subtraction trick rather than just mention it.

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AI HintsAI Generated

Suggested Approach

Start by writing the forward pass equations for scaled dot-product attention, explicitly including the scaling factor and masking before softmax. Then implement a numerically stable softmax by subtracting the row-wise maximum, and finally analyze time and memory complexity and discuss edge cases like all-masked rows and extreme values.

Pro tip: Mention that masking is applied by adding a large negative value (e.g., -1e9) to masked positions before softmax, and that for fully masked rows you should handle the division by zero to avoid NaNs. This shows attention to numerical stability and production readiness.

1. Write the equations

Clearly state the scaled dot-product attention formula: Attention(Q, K, V) = softmax(Q K^T / sqrt(d_k) + M) V, where M is the mask (0 for allowed, -inf for masked).

2. Implement numerically stable softmax

For each row, subtract the maximum value before exponentiation to prevent overflow, then normalize by the sum of exponentials.

3. Apply masking

Add the mask (with large negative values for masked positions) to the scaled scores before softmax, ensuring masked positions get near-zero attention weights.

4. Compute the output

Multiply the attention weights by V to get the final output, and handle any edge cases like all-masked rows by setting their output to zero or a small epsilon.

5. Analyze complexity and edge cases

Discuss time complexity O(n^2 d) and memory O(n^2) for sequence length n and dimension d, and cover edge cases such as all-masked rows, very large scores, and zero-length sequences.

Key Points to Mention

Scaling factor 1/sqrt(d_k) to prevent softmax saturation
Numerically stable softmax: subtract row-wise max before exponentiation
Masking with large negative values (e.g., -1e9) before softmax
Handling fully masked rows to avoid division by zero (e.g., set output to zero)
Time complexity O(n^2 d) and memory O(n^2) due to attention matrix
Edge cases: all-masked rows, extreme values causing overflow/underflow, empty sequences

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