Openai·Software Engineer·Technical Phone Screen·Senior

Senior

May 2026

Summary

OpenAI SWE interview that went deep on transformer internals, specifically building a decoder block with KV caching for autoregressive generation. Pretty technical and unforgiving if you haven't actually implemented one before.

Questions Asked (1)

Implement a transformer decoder block that uses a KV cache to speed up autoregressive generation. Walk through how shapes evolve per decoding step, what the memory cost looks like, and how you'd verify correctness against a naive full recompute.

System DesignTechnical Trade-offsAlgorithms & Data Structures

Author's notes

This one has a lot of surface area and I didn't pace myself well.

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AI HintsAI Generated

Suggested Approach

Start by explaining the high-level architecture of a transformer decoder block and the role of KV caching in autoregressive generation. Then, walk through the shape transformations step-by-step for a single decoding step, highlighting how the cache is updated and used. Finally, discuss memory implications and propose a verification strategy comparing cached outputs to a naive full recompute.

Pro tip: Emphasize that KV caching trades memory for speed, and quantify the memory cost per layer and per token to show you understand the practical constraints. Also, mention that correctness verification should include both numerical equality (within tolerance) and performance benchmarking to ensure the cache actually speeds up generation.

1. Explain the decoder block and KV cache concept

Briefly describe the transformer decoder block components (self-attention, cross-attention if applicable, feed-forward) and how KV caching avoids recomputing keys and values for past tokens during autoregressive generation.

2. Walk through shape evolution per decoding step

For a single decoding step, detail the input shapes (e.g., new token embedding: [batch, 1, d_model]), how queries, keys, and values are computed, and how the cache is concatenated along the sequence dimension. Show the resulting attention output shape and how it feeds into subsequent layers.

3. Analyze memory cost

Compute the memory footprint of the KV cache: per layer, per token, it stores two tensors of shape [batch, num_heads, seq_len, head_dim]. Discuss scaling with sequence length, batch size, and model size, and mention techniques like quantization or paging to mitigate memory pressure.

4. Propose correctness verification

Outline a test that runs a naive full recompute (without cache) and compares the outputs token-by-token with the cached version, ensuring numerical closeness (e.g., using torch.allclose). Also, suggest measuring latency and memory usage to validate the speedup and overhead.

5. Discuss trade-offs and edge cases

Mention trade-offs such as increased memory usage versus reduced computation, and edge cases like handling the initial prompt (prefill) and cache initialization, as well as potential issues with variable sequence lengths in batched generation.

Key Points to Mention

KV cache stores keys and values for all past tokens, avoiding recomputation during generation.
Shape evolution: from [batch, seq_len, d_model] to per-step [batch, 1, d_model] for queries, with keys/values cached as [batch, num_heads, past_len, head_dim].
Memory cost: 2 * batch * num_heads * seq_len * head_dim * num_layers * bytes_per_param, which grows linearly with sequence length.
Correctness verification: compare cached vs. non-cached outputs using numerical tolerance (e.g., 1e-5) and ensure identical token predictions.
Performance considerations: caching reduces FLOPs but increases memory bandwidth usage; optimize with techniques like multi-query attention or cache quantization.
Implementation details: cache initialization, concatenation along sequence dimension, and handling of attention masks to prevent attending to future tokens.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.