---
title: "Interview questions"
description: "Questions organized by technical track, difficulty, and source evidence."
canonical_url: "https://fanout.sh/inference-eng/interviews"
md_url: "https://fanout.sh/inference-eng/interviews.md"
access: "public"
---

# Interview questions

Questions organized by technical track, difficulty, and source evidence.

## Public overview

Implement a PyTorch autoregressive inference loop, then explain where and how you would add KV-cache reuse. The strong answer separates prefill from decode, appends K/V per step instead of recomputing, and can state the cache tensor shapes.

Implement batched multi-head self-attention with a causal mask, without using a prebuilt attention module. Shapes end to end, (B, H, T, d_k), scaling by the root of d_k, masking before softmax, and the final output projection.

Debug a pre-norm transformer block containing an incorrect residual or normalization path. Know the dataflow cold: x + Attn(LN(x)), then x + MLP(LN(x)). Planted bugs usually move the LayerNorm or drop a residual.

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