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Walmart Labs·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Walmart Labs SWE interview, just one coding question the whole time. Pretty straightforward session but it definitely tests whether you actually know your sorting algorithms cold.

Questions Asked (1)

Q1

Implement merge sort from scratch.

Algorithms & Data Structures
Author's notes

I've seen this question a dozen times and still fumbled the merge step a bit under pressure.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and constraints, then explain the divide-and-conquer strategy of merge sort. Walk through the implementation step-by-step, covering the merge function and recursion, and analyze time/space complexity. Finally, discuss optimizations and edge cases.

Pro tip: Mention that merge sort is stable and often used for external sorting due to its sequential access pattern, which is relevant for large-scale systems like Walmart Labs. Also, be prepared to discuss iterative vs recursive implementations and trade-offs.

1. Clarify requirements and constraints

Ask about input size, data types, memory constraints, and whether stability is required. This shows you consider practical aspects before coding.

2. Explain the algorithm

Describe the divide-and-conquer approach: recursively split the array into halves until single elements, then merge sorted halves. Emphasize the merge step as the core operation.

3. Implement the merge function

Write a helper function that takes two sorted subarrays and merges them into a single sorted array. Use temporary arrays or in-place merging with pointers.

4. Implement recursive merge sort

Write the recursive function that splits the array and calls merge. Handle base cases (array of size 0 or 1).

5. Analyze complexity and discuss optimizations

State time complexity O(n log n) and space complexity O(n). Mention optimizations like using insertion sort for small subarrays and avoiding unnecessary copying.

Key Points to Mention

  • Divide-and-conquer paradigm and recursive structure
  • Stability of merge sort and its importance in certain applications
  • Time complexity O(n log n) and space complexity O(n)
  • Comparison with other sorting algorithms (e.g., quicksort, heapsort)
  • Handling edge cases: empty array, single element, duplicates
  • Iterative bottom-up implementation as an alternative to recursion

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