Live Coding: Multi-Head Self-Attention

Live Coding Multi-Head Self-Attention (in zero-dependency Pure Numpy or PyTorch) is the premier benchmark live-coding problem in MLE senior interviews; candidates must implement the full attention pipeline from scratch: 1) Linear Projections: projecting input $X \in \mathbb{R}^{B \times L \times d_{\text{model}}}$ into $Q, K, V$; 2) Multi-Head Tensor Reshaping: reshaping and transposing to $(B, H, L, d_k)$ where $d_k = d_{\text{model}} / H$; 3) Scaled Dot-Product Scoring: computing $S = \frac{Q K^T}{\sqrt{d_k}}$ and applying upper-triangular causal masks with $-\infty$; 4) Numerically Stable Softmax and Value aggregation: $\text{Attention}(Q,K,V) = \text{softmax}(S) V$; 5) Multi-head tensor concatenation and final output linear projection $W_O$.

Senior MLE whiteboard coding interviews, custom PyTorch operator development, and debugging Transformer tensor dimensions.

Verifies whether an engineer deeply grasps tensor dimension transformations, einsum broadcasting, scaling factor mathematical variance proofs, and causal masking mechanics beyond black-box APIs.

Test Your Knowledge: Practice Questions for "Live Coding: Multi-Head Self-Attention"

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