Adobe·Machine Learning Engineer·Technical Phone Screen
Apr 2026
Adobe ML Engineer technical screen focused heavily on transformer internals, specifically Multi-Head Attention. The depth they expected was real, not just surface-level definitions. Came out feeling like I'd either nailed it or completely missed what they were looking for.
- Walk through the full operation order inside Multi-Head Attention, step by step.
- Explain the Q, K, V projections and how the tensors get reshaped for multi-head computation.
- How are attention scores computed and what role does softmax play?
- Why does Multi-Head Attention use four linear projections total rather than three?
- Do a FLOPs analysis of the self-attention mechanism.
- Why is the computational complexity of self-attention O(S^2) with respect to sequence length?
“I started with the Q/K/V projections and worked forward, but I stumbled a bit on where exactly the reshape happens relative to the score computation.” The rest of the author's notes on Machine Learning Engineer interview at Adobe, Technical Phone Screen round, covers how they worked through the question, what the panel pushed back on, and what they would do differently.
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