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About this interview
A technical interview on Transformer Architecture, pitched at the medium level. A voice AI interviewer leads the conversation, adapts its questions to your answers, keeps you on topic, and afterward gives you honest, specific feedback on where you were strong and where to improve. Expect roughly 30 minutes.
What you'll be assessed on
Explain scaled dot-product attention and why scaling by root-dk is necessary
Describe multi-head attention, positional encodings (absolute, learned, RoPE, ALiBi), and their tradeoffs
Articulate the role of pre-norm vs post-norm, RMSNorm, and SwiGLU activations in modern Transformers
Explain the KV cache, its memory implications at long context, and FlashAttention's IO-aware design
Topics covered
Motivation for AttentionQKV SemanticsScaled Dot-Product AttentionAttention ScalingAttention MaskingMulti-Head AttentionPositional EncodingsRoPE and ALiBiTransformer Block StructurePre-Norm vs Post-NormRMSNormSwiGLU and ActivationsKV CacheKV Cache Memory
A few sample questions
Just examples to set expectations - the real interview has many more and adapts to your responses.
“At a high level, what problem does the attention mechanism solve that earlier architectures like RNNs and LSTMs struggled with?
“Describe what a Transformer encoder block looks like end to end. What components does it contain, and in what order do they appear?
“What happens to training stability if you remove the residual connections from a deep Transformer? How did the nndl experiments on this illustrate the degradation?