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Optimize attention for long sequences

AmazonSoftware Engineer · Technical Screen
HardSoftware EngineerTechnical ScreenML System Design

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Optimize attention for long sequences evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Survey efficient attention families for long sequences (e.g., sparse/sliding-window, low-rank/kernelized, recurrent/state-space approaches, KV-cache variants) and compare accuracy–throughput–memory trade-offs. Explain at a high level why FlashAttention improves performance (IO-aware tiling to reduce HBM traffic, fused kernels, selective recomputation) and where it helps most; discuss constraints such as sequence length, head dimension, memory bandwidth, and numerical stability considerations.