Openai·Machine Learning Engineer·Technical Phone Screen·Senior

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Jun 2026Remote

Summary

Went through a technical screen for an MLE role at OpenAI and got hit with a deep systems question on KV caching. Not a leetcode grind session, more like a whiteboard design conversation that kept going deeper every time I thought I'd covered it.

Questions Asked (1)

During transformer inference, what is a key-value cache? Walk through what gets stored, why it speeds up autoregressive decoding, the difference between the prefill and decode phases, the memory tradeoffs, and at least two production-level optimizations.

System DesignTechnical Trade-offs

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AI HintsAI Generated

Suggested Approach

Start by defining the KV cache and its role in autoregressive decoding, then contrast prefill and decode phases to highlight computational differences. Discuss memory tradeoffs and production optimizations like paged attention and quantization, tying each to real-world impact.

Pro tip: Emphasize that the KV cache shifts the bottleneck from compute to memory bandwidth, so optimizations often focus on memory efficiency rather than raw FLOPs.

1. Define KV Cache

Explain that the KV cache stores key and value tensors from previous tokens to avoid recomputation during autoregressive generation.

2. Explain Speedup Mechanism

Describe how caching reduces the complexity of generating each new token from O(n^2) to O(n) by reusing past computations.

3. Differentiate Prefill and Decode

Contrast the prefill phase (parallel processing of the prompt, compute-bound) with the decode phase (sequential token generation, memory-bound).

4. Discuss Memory Tradeoffs

Analyze how cache size scales with sequence length, batch size, and model dimensions, and the resulting GPU memory constraints.

5. Present Production Optimizations

Describe at least two optimizations like PagedAttention, quantization, or multi-query attention, and their benefits in deployment.

Key Points to Mention

KV cache stores key and value tensors for each layer and attention head.
Autoregressive decoding benefits from caching by avoiding redundant computation of past tokens.
Prefill phase processes the entire prompt in parallel, while decode phase generates one token at a time.
Memory usage grows linearly with sequence length and batch size, often becoming the bottleneck.
PagedAttention optimizes memory fragmentation and enables efficient memory sharing.
Quantization of KV cache reduces memory footprint with minimal accuracy loss.

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