Openai·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

May 2026

Summary

OpenAI ML engineer interview, technical round focused on transformer internals. They gave you a class skeleton and expected working code plus a real discussion about memory tradeoffs. Not a vibe check at all.

Questions Asked (1)

You're given a Transformer and attention layer skeleton. Implement a KV cache for autoregressive inference: cache K and V tensors per layer, append new token's K/V each step, run attention over the full cache, and make sure position embeddings reflect the correct offsets. Then discuss memory cost and how batched generation changes things.

System DesignTechnical Trade-offsAlgorithms & Data Structures

Author's notes

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AI HintsAI Generated

Suggested Approach

Start by outlining the KV cache design: allocate per-layer caches for K and V, append new K/V each step, and compute attention using the full cache. Then discuss memory implications and how batching affects cache management, emphasizing trade-offs and optimizations.

Pro tip: Mention that KV cache trades memory for speed by avoiding recomputation, and highlight that in batched generation, you must handle variable sequence lengths and padding efficiently, often using techniques like paged attention or continuous batching.

1. Design the KV Cache

Explain that for each layer, you maintain a cache tensor for keys and values, initialized empty and appended with the new token's K/V at each step. Ensure the cache shape accommodates batch size, sequence length, and head dimension.

2. Implement Attention with Cache

At each step, compute attention using the current query and the full cached K/V. Use scaled dot-product attention, applying a causal mask to prevent attending to future tokens.

3. Handle Position Embeddings

Add position embeddings to the input token based on its absolute position in the sequence. When using the cache, ensure the position offset is correctly tracked so that the new token gets the right positional encoding.

4. Analyze Memory Cost

Discuss that memory scales with batch size, sequence length, number of layers, and model dimension. For large models and long sequences, the cache can become a bottleneck, requiring optimizations like quantization or eviction policies.

5. Address Batched Generation

Explain that batching increases memory linearly with batch size, and handling variable-length sequences requires padding or more advanced techniques like continuous batching to avoid wasted computation.

Key Points to Mention

KV cache eliminates redundant computation of past keys and values, speeding up autoregressive decoding.
Memory cost is O(batch_size * seq_len * num_layers * d_model), which can be significant for long sequences.
Position embeddings must be offset correctly; using relative positions or rotary embeddings can simplify this.
Batched generation requires managing caches per sequence, often with padding and attention masks.
Advanced techniques like paged attention or memory-efficient attention can reduce memory fragmentation.
Trade-offs: larger batch size improves throughput but increases memory; cache eviction or compression can help.

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