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moveworks·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Jun 2026

Summary

Got a coding problem at Moveworks for a software engineer role that was basically a multi-step string filtering and transformation pipeline. It looked like a clean algorithm question on the surface but had enough edge cases and layered logic that it took a while to even fully parse what was being asked.

Questions Asked (1)

Q1

Given a list of strings and a pairwise similarity function, implement a multi-step pipeline: first filter strings by the proportion of their most-frequent characters, then apply a similarity-based filter on the remaining candidates, then remove characters shared with any other original input string, and finally concatenate the results.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

This one took me an embarrassingly long time just to understand.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements and edge cases, then outline a modular pipeline where each step is a separate function. Discuss the time and space complexity of each step and the overall pipeline, and consider trade-offs between clarity and performance.

Pro tip: Emphasize the importance of defining the similarity function's contract and handling edge cases like empty strings or ties in character frequency. Also, mention that you would write unit tests for each stage to ensure correctness.

1. Clarify Requirements

Ask questions to understand the exact definitions: what is 'proportion of most-frequent characters'? How is similarity threshold determined? What does 'remove characters shared with any other original input string' mean precisely?

2. Design Pipeline Stages

Break down the problem into four distinct stages: frequency-based filter, similarity-based filter, character removal, and concatenation. Define the input and output of each stage.

3. Analyze Complexity

For each stage, determine the time and space complexity. Consider optimizations like using hash maps for frequency counts and early termination in similarity checks.

4. Implement and Test

Write clean, modular code for each stage. Test with edge cases such as empty list, strings with all same characters, and similarity function returning edge values.

5. Discuss Trade-offs

Talk about potential trade-offs: e.g., filtering early vs. late, using approximate similarity for performance, and handling large inputs with streaming or parallel processing.

Key Points to Mention

  • Definition and computation of character frequency proportion (e.g., max frequency / length).
  • Contract of the pairwise similarity function (symmetric, range, etc.) and how to apply it as a filter (e.g., threshold or top-k).
  • Efficient data structures: hash maps for frequency, sets for character removal.
  • Time and space complexity of each stage and overall pipeline.
  • Edge cases: empty strings, ties in frequency, similarity ties, and strings with no shared characters.
  • Modularity and testability: separating concerns for easier debugging and maintenance.

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