Amazon·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

Apr 2026

Summary

Amazon Applied Scientist interview with a pretty rough algorithmic question that I couldn't crack even with hints. The interviewer was patient but I left feeling like I'd wasted both our time.

Questions Asked (1)

Given K documents and G GPUs where 0 <= K < G, design an optimal batching strategy that groups documents into buckets to minimize padding waste across GPUs.

Algorithms & Data StructuresTechnical Trade-offsSystem Design

Author's notes

I'd actually seen bucketing before in my PhD work so I wasn't completely lost on the concept.

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AI HintsAI Generated

Suggested Approach

First, clarify the problem constraints and assumptions, such as whether documents have known lengths and if GPUs can process multiple buckets concurrently. Then, propose a greedy algorithm that sorts documents by length and assigns them to buckets to minimize padding, and analyze its time complexity and optimality. Finally, discuss trade-offs and potential improvements like dynamic programming or approximation algorithms for large-scale scenarios.

Pro tip: Emphasize that minimizing padding waste is equivalent to maximizing GPU utilization, and relate it to Amazon's cost-efficiency and customer obsession principles. Mention that in practice, you might also consider load balancing and communication overhead, not just padding.

1. Clarify requirements and constraints

Ask about document length distribution, GPU memory limits, whether documents can be split, and if the number of buckets is fixed. Confirm that K < G means some GPUs will be idle, so the goal is to use as few GPUs as possible or balance load.

2. Formalize the optimization problem

Define padding waste as the sum over buckets of (max length in bucket * bucket size - sum of document lengths). The objective is to partition K documents into at most G buckets to minimize total padding waste.

3. Propose a greedy algorithm

Sort documents by length descending. For each document, place it into the bucket that minimizes the increase in padding waste (or equivalently, the bucket with the smallest current max length that can accommodate it). This is a variant of the bin packing or scheduling problem.

4. Analyze complexity and optimality

The greedy approach runs in O(K log K + K * G) time. Discuss that it is not always optimal but provides a good approximation. Mention that the problem is NP-hard in general, so exact solutions are impractical for large K.

5. Discuss trade-offs and alternatives

Consider dynamic programming for small K, or approximation algorithms with guarantees. Also discuss practical considerations like GPU memory fragmentation, batch size limits, and the cost of idle GPUs.

Key Points to Mention

Sorting documents by length to group similar lengths together reduces padding.
Greedy assignment to the bucket with the smallest current max length minimizes padding increase.
The problem is NP-hard (related to bin packing), so exact optimality is not feasible for large K.
Time complexity: O(K log K + K * G) for the greedy approach.
Trade-off between using fewer GPUs (more padding per GPU) vs. using more GPUs (less padding but higher cost).
Practical considerations: GPU memory limits, batch size constraints, and potential for dynamic batching.

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