Adobe·Machine Learning Engineer

Adobe Machine Learning Engineer interview experience

Hard

Neutral

Candidate report, summarized by Dataford

The interview hit hard on advanced transformer internals and inference efficiency.

I was asked to explain differences between encoder and decoder transformer setups, clarify why bidirectional attention differs from causal masked attention, and walk through how encoder-decoder cross attention behaves.

Then it moved into multi-head attention mechanics—how Q/K/V projections work and the order of operations to compute attention scores. I also got questioned on KV cache and why it matters for decoder-only inference, including the trade-offs in memory growth. From there, it broadened into transformer system performance, distributed training infrastructure, and optimization

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