Openai·Machine Learning Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

Apr 2026Remote

Summary

ML coding round at OpenAI focused entirely on KV caching for Transformer inference. Pretty deep technically, more systems-aware than a typical coding screen.

Questions Asked (1)

Implement KV caching for a multi-head self-attention layer. At each generation step, append the new token's key and value to a per-layer cache and attend over the full cached sequence instead of recomputing K and V for past tokens.

System DesignAlgorithms & Data StructuresTechnical Trade-offs

Author's notes

The core implementation wasn't the hard part.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem scope and assumptions, then outline the standard KV cache design for multi-head self-attention, including data structures and update logic. Finally, discuss trade-offs, optimizations, and potential pitfalls to demonstrate depth.

Pro tip: Emphasize that KV caching is only valid for autoregressive decoding and that you must handle the initial prompt phase separately. Also, mention memory layout optimizations like contiguous buffers or paged attention to show production awareness.

1. Clarify requirements and assumptions

Confirm that the task is for inference-time autoregressive generation, not training, and discuss batch size, sequence length, and hardware constraints.

2. Design the cache data structure

Propose per-layer caches storing keys and values for each head, with shape [batch, num_heads, seq_len, head_dim], and explain how to append new entries efficiently.

3. Implement the forward pass with caching

Describe how to compute Q, K, V for the new token, append K and V to the cache, and then compute attention using the full cached K and V.

4. Address memory and performance trade-offs

Discuss memory growth, pre-allocation strategies, and optimizations like paged attention or sliding window to handle long sequences.

5. Validate and test

Mention unit tests comparing cached vs. non-cached outputs, and profiling to ensure speedup and correctness.

Key Points to Mention

Autoregressive decoding: only the new token's K and V are computed, past ones are reused.
Cache shape and layout: per layer, per head, with batch dimension; often stored as separate tensors for K and V.
Attention computation: Q from current token attends to all cached K and V, using scaled dot-product.
Memory management: cache grows linearly with sequence length; consider pre-allocation or dynamic growth.
Optimizations: paged attention, sliding window, or quantization to reduce memory footprint.
Correctness: ensure causal masking is preserved and that the cache is updated in the right order.

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