Amazon·Machine Learning Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

Apr 2026

Summary

Amazon ML Engineer interview that leaned pretty heavily on algorithm fundamentals. The main focus was a classic top-K problem and they wanted you to walk through multiple solutions, not just code one up.

Questions Asked (1)

Given an integer array, find the top K largest elements. Walk through different approaches and compare their time and space trade-offs.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The part that tripped me up was they didn't just want code, they wanted a real comparison.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints (e.g., array size, K value, memory limits) and then present multiple approaches: sorting, min-heap, and quickselect. Compare their time and space complexities, and discuss which is optimal for different scenarios, especially in a machine learning context where large datasets are common.

Pro tip: Mention that for streaming data or when K is small, a min-heap of size K is often the best choice because it processes data in one pass with O(N log K) time and O(K) space, which is practical for ML pipelines. Also, note that quickselect has O(N) average time but O(N) worst-case, so randomization or median-of-medians can mitigate that.

1. Clarify requirements and constraints

Ask about input size, whether the array fits in memory, if K is small relative to N, and if the order of the top K matters. This shows you consider practical constraints before diving into solutions.

2. Present brute-force and sorting approaches

Describe sorting the array and taking the last K elements: O(N log N) time, O(1) or O(N) space depending on sort. Mention that this is simple but inefficient for large N or small K.

3. Introduce heap-based approach

Explain using a min-heap of size K: iterate through the array, push elements, and if heap size exceeds K, pop the smallest. This gives O(N log K) time and O(K) space, which is efficient when K is small.

4. Discuss quickselect (partition-based) approach

Describe quickselect to find the K-th largest element in average O(N) time and O(1) extra space (if in-place). Note worst-case O(N^2) and how randomization or median-of-medians can improve it.

5. Compare trade-offs and recommend

Summarize time/space complexities and discuss when to use each: sorting for simplicity, heap for streaming or small K, quickselect for optimal average performance. Relate to ML scenarios like feature selection or top-K accuracy.

Key Points to Mention

Time and space complexity of each approach: sorting O(N log N), heap O(N log K), quickselect O(N) average.
Space complexity: heap uses O(K) extra space, quickselect can be in-place O(1), sorting may use O(N) depending on algorithm.
Stability and order: if the top K need to be sorted, additional O(K log K) may be needed.
Handling duplicates and edge cases: K > N, K = 0, negative numbers, etc.
Streaming data: heap approach works well for online processing, while sorting and quickselect require the full array.
Practical ML applications: top-K accuracy, feature importance, recommendation systems, and handling large-scale data.

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