Google·Software Engineer·Technical Phone Screen·Senior

Senior

Jun 2026

Summary

Google SWE interview with an ML coding round where I had to implement a transformer block forward pass from scratch using weight matrices. No fancy libraries, just raw math and numpy-style thinking. Pretty intense for a coding round.

Questions Asked (1)

Implement a simplified Transformer block forward pass given input tensor X and weight matrices for single-head self-attention and a feed-forward network. Compute Q, K, V projections, scaled dot-product attention scores, apply an optional mask before softmax, then pass through the FFN to produce output Y.

Algorithms & Data StructuresTechnical Trade-offsSystem Design

Author's notes

This one took me a minute to get my footing.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the input shapes and weight matrix dimensions, then implement the forward pass step-by-step: Q, K, V projections, scaled dot-product attention with optional mask, softmax, weighted sum of V, and finally the FFN. Emphasize numerical stability and vectorization for efficiency.

Pro tip: Mention that you would scale the dot products by 1/sqrt(d_k) before softmax to prevent vanishing gradients, and use a large negative value (e.g., -1e9) for masked positions to ensure zero attention weights after softmax.

1. Clarify dimensions and inputs

Confirm the shapes of X (batch_size, seq_len, d_model) and weight matrices (W_q, W_k, W_v, W_ff1, W_ff2) to ensure correct matrix multiplications. Ask about mask shape and whether bias terms are included.

2. Compute Q, K, V projections

Perform linear projections: Q = X @ W_q, K = X @ W_k, V = X @ W_v. Optionally add bias terms if provided.

3. Compute scaled dot-product attention

Calculate attention scores = (Q @ K^T) / sqrt(d_k). Apply mask by setting masked positions to a large negative value (e.g., -1e9) before softmax. Then compute attention weights = softmax(scores) and output = attention_weights @ V.

4. Apply feed-forward network

Pass the attention output through the FFN: FFN(x) = max(0, x @ W_ff1 + b1) @ W_ff2 + b2, or use another activation if specified. This typically expands then contracts the dimension.

5. Return final output Y

Combine the steps to produce Y, ensuring the output shape matches expectations (batch_size, seq_len, d_model). Mention any residual connections or layer normalization if part of the block.

Key Points to Mention

Scaling factor 1/sqrt(d_k) for numerical stability and preventing softmax saturation.
Masking technique: add large negative value before softmax to zero out attention for masked positions.
Efficiency: use batch matrix multiplication (e.g., torch.bmm or np.einsum) to avoid loops.
FFN architecture: typically two linear layers with a ReLU activation in between, expanding to 4*d_model then back.
Residual connections and layer normalization (if applicable) to stabilize training and improve gradient flow.
Handling of batch dimension: ensure operations broadcast correctly across batch and sequence length.

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