Moveworks Software Engineer Interview Questions
6+ questions from real Moveworks Software Engineer interviews, reported by candidates.
Round Types
Top Topics
Questions
#140 Word Break II
LeetCode #140: Word Break II. Difficulty: Hard. Topics: Array, Hash Table, String, Dynamic Programming, Backtracking, Trie, Memoization. Asked at Moveworks in the last 6 months.
LeetCode #3076: Shortest Uncommon Substring in an Array. Difficulty: Medium. Topics: Array, Hash Table, String, Trie. Asked at Moveworks in the last 6 months.
## Problem You are given a block of text containing inline citations in the format `[AuthorYear]` (e.g., `[Smith2019]`, `[LeCun1989]`). Extract all unique citations, parse them into structured form, and return them sorted by year then author. ```python import re from dataclasses import dataclass @dataclass class Citation: author: str year: int raw: str def parse_citations(text: str) -> list[Citation]: pass ``` ``` Input: "Recent work [Smith2019] builds on [LeCun1989] and [Vaswani2017]. See also [Smith2019] for further detail." Output: [ Citation(author='LeCun', year=1989, raw='[LeCun1989]'), Citation(author='Vaswani', year=2017, raw='[Vaswani2017]'), Citation(author='Smith', year=2019, raw='[Smith2019]'), ] # Deduplicated, sorted by year. ``` ## Follow-ups 1. How would you extend the regex to handle multi-author citations like `[Smith&Jones2020]` or `[Smith et al. 2020]`? 2. What if the year appears before the author (e.g., `[2019Smith]`)? How do you detect the format? 3. How would you link each citation back to a list of references at the end of the document? 4. Extend to extract citation context: the sentence in which each citation appears.
## Problem Compute similarity between two strings using edit distance or similarity metrics. ## Likely LeetCode equivalent Similar to LC 72 Edit Distance. ## Tags coding, strings, dynamic_programming, onsite
## Problem Given a sentence and a dictionary mapping words to their replacements, rewrite the sentence by substituting every occurrence of a dictionary key with its value. Matching is case-insensitive but preserve the original casing pattern (all-caps, title-case, or lowercase) in the output. ```python def text_rewrite(sentence: str, substitutions: dict[str, str]) -> str: pass def apply_case(original: str, replacement: str) -> str: """Match the case pattern of original in the replacement string.""" pass ``` ``` Input: sentence = "The Quick BROWN fox jumps" substitutions = {"quick": "slow", "brown": "red", "fox": "cat"} Output: "The Slow RED cat jumps" # "Quick" -> title case -> "Slow" # "BROWN" -> all caps -> "RED" # "fox" -> lowercase -> "cat" ``` ## Follow-ups 1. How do you handle substitutions where the replacement is a multi-word phrase and case patterns differ per word? 2. What if dictionary keys overlap (e.g., `"cat"` and `"catch"`)? Define a disambiguation rule. 3. How would you process a file too large to fit in memory? 4. Extend to support regex-based substitution patterns in the dictionary keys.
## Problem Given a string `s`, find the longest substring that contains no repeated characters. Return both the length and the substring itself. If there are multiple substrings of the same maximum length, return the one that starts earliest. ```python def longest_unique_substring(s: str) -> tuple[int, str]: pass ``` ``` Input: s = "abcabcbb" Output: (3, "abc") Input: s = "bbbbb" Output: (1, "b") Input: s = "pwwkew" Output: (3, "wke") Input: s = "" Output: (0, "") ``` ## Follow-ups 1. Implement this using the sliding window technique. What is the time and space complexity? 2. How does replacing the character set check with a hash map of last-seen positions improve the approach? 3. Extend to find the longest substring with at most `k` distinct characters. 4. How would you find ALL substrings of the maximum length, not just the first one?
What Moveworks Looks for in Software Engineer Interviews
Moveworks Software Engineer interviews are calibrated against the level and scope expected of the role. Across 6+ verified candidate reports on LeakCode, the consistent signals interviewers look for: clear problem decomposition before coding, explicit complexity reasoning, structured handling of edge cases, and the ability to articulate trade-offs between two reasonable approaches.
The discriminator between candidates who advance and candidates who do not is rarely the final correctness of the solution. It is the path to the solution: did you ask clarifying questions, did you state your approach before coding, did you handle edge cases without prompting, and did you communicate your reasoning throughout. Reports tagged "no hire" frequently cite a working solution with poor communication; reports tagged "strong hire" cite clear thinking even when the final solution was incomplete.
How To Use This Question Set
Real interview reports are a calibration tool, not a memorization target. Companies update their question pools every 2-4 months; memorizing exact problems risks misleading you when the interviewer uses a variant. The high-leverage use: identify the patterns that appear repeatedly in Moveworks Software Engineer reports, practice those patterns on similar (not identical) problems, and use the reports to understand the interviewer's typical follow-up depth.
Filter the questions below by round type, difficulty, and recency. Focus first on reports from the past 6-12 months; older reports may reference questions that have since rotated out of Moveworks's pool. Reports tagged with quantified difficulty (e.g., "medium-hard") are higher-signal than reports without difficulty tags.
Round-by-Round Expectations
Moveworks Software Engineer loops typically span 4-6 rounds across phone screens and on-site or virtual on-site interviews. The structure varies by company: some run 1 recruiter screen + 1 technical phone + 3-4 on-site rounds; others run 1 recruiter screen + 1 OA + 4-5 on-site rounds. The recruiter screen is logistics and culture-light; the technical phone screen is medium-difficulty coding; the on-site loop covers coding, system design (at L4+ levels), and behavioral rounds.
Each round is designed to surface a specific signal. Coding rounds: correctness, code quality, complexity reasoning, communication. System design rounds: requirements clarification, design judgment, operational thinking. Behavioral rounds: ownership scope, leadership, ambiguity tolerance, conflict navigation. Strong candidates explicitly hit each signal dimension out loud during the round; weak candidates focus only on solving the prompt.
Common Interview Mistakes At This Combination
Reports tagged "no hire" at Moveworks Software Engineer commonly cite: jumping into code without clarifying requirements, coding silently for 10+ minutes without verbalizing approach, missing edge cases (empty input, single element, very large input, overflow), and producing a working solution that the candidate cannot explain or refactor when probed. Strong candidates avoid these patterns by following a consistent template: clarify, verbalize approach, code with narration, test with examples.
Behavioral and design rounds have their own failure modes. Behavioral: stories that use "we" instead of "I" diluting individual signal, stories with no quantified outcome, defensiveness when probed about failure. Design: not asking clarifying questions, not stating requirements out loud, designing for a single server when the prompt clearly implies scale, ignoring operational concerns (deployment, monitoring, rollback). These show up in roughly half of Moveworks Software Engineer interview retrospectives on LeakCode.
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