Microsoft·Software Engineer·Technical Phone Screen·Senior

Senior

Apr 2026

Summary

Microsoft data science interview with a question on transformer architecture. Pretty technical, felt like a screen to see if you actually know the internals or just use the libraries.

Questions Asked (1)

In the transformer architecture, how does the decoder differ from the encoder?

System DesignTechnical Trade-offs

Author's notes

I knew the broad strokes but fumbled explaining masked self-attention clearly.

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AI HintsAI Generated

Suggested Approach

Start by defining the encoder and decoder roles in the transformer, then contrast their architectural components and information flow. Highlight how the decoder's self-attention is masked and includes cross-attention to the encoder, and explain the implications for training and inference.

Pro tip: Emphasize that the decoder's masked self-attention enables autoregressive generation, which is crucial for tasks like machine translation and text generation, and mention how this affects parallelization and latency in production systems.

1. Define encoder and decoder roles

Explain that the encoder processes the input sequence bidirectionally to create contextual representations, while the decoder generates the output sequence autoregressively.

2. Compare attention mechanisms

Describe that the encoder uses unmasked self-attention, whereas the decoder uses masked self-attention to prevent attending to future tokens, and also includes cross-attention over the encoder's output.

3. Discuss architectural differences

Note that each decoder layer has three sub-layers (masked self-attention, cross-attention, feed-forward) versus the encoder's two (self-attention, feed-forward), and that both use residual connections and layer normalization.

4. Explain training and inference implications

Mention that the encoder can process all input tokens in parallel, while the decoder must generate tokens sequentially during inference, though teacher forcing allows parallel training.

5. Relate to system design trade-offs

Connect these differences to practical considerations like latency, throughput, and memory usage in deployment, especially for real-time applications.

Key Points to Mention

Encoder uses bidirectional self-attention; decoder uses masked (causal) self-attention.
Decoder includes an additional cross-attention layer that attends to the encoder's output.
Decoder generates tokens autoregressively, one at a time during inference.
Encoder processes the entire input sequence in parallel.
Both encoder and decoder layers use residual connections, layer normalization, and position-wise feed-forward networks.
The masking in decoder self-attention ensures that predictions for a given position depend only on known outputs at earlier positions.

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