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Google·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
May 2026

Summary

Google SWE coding round, one question the whole time. The problem looked like a string parsing thing but there was more going on once you got into the matching logic.

Questions Asked (1)

Q1

Given a text string and a dictionary mapping token strings to IDs, scan the text left to right and produce an output sequence using greedy longest-prefix matching. At each position, if any token matches, pick the longest one and emit its ID; otherwise emit the raw character. Follow-up: how would you redesign this if matching priority became configurable (e.g. insertion order, a score, or a custom comparator)?

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

I got the basic greedy scan working without too much trouble.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints (e.g., token lengths, dictionary size, text length) and then describe a greedy left-to-right scan using a trie for efficient longest-prefix matching. For the follow-up, discuss how to decouple the matching logic from the priority rule by introducing a comparator or priority function, and analyze trade-offs between different data structures.

Pro tip: Mention that a trie can be augmented with a priority value at each terminal node to handle configurable priorities without changing the core matching algorithm. Also, discuss the time complexity: O(n * L) where n is text length and L is max token length, and how a trie reduces the constant factor.

1. Clarify requirements and constraints

Ask about token length limits, dictionary size, text length, and whether overlapping tokens are allowed. Confirm that greedy longest-prefix matching means always taking the longest match at each position.

2. Design the basic greedy algorithm

Describe a naive approach: at each position, try all possible token lengths from longest to shortest and check if the substring is in the dictionary. If found, emit its ID and advance; else emit the character and advance by one.

3. Optimize with a trie

Explain how to build a trie from the dictionary to efficiently find the longest matching token starting at a given position. Traverse the trie character by character, keeping track of the last terminal node encountered.

4. Address the follow-up: configurable priority

Discuss how to modify the trie to store a priority value at each terminal node. When multiple tokens match, instead of always picking the longest, pick the one with the highest priority according to the configured rule (e.g., insertion order, score, custom comparator).

5. Analyze trade-offs and edge cases

Compare approaches: trie vs. hash set with length iteration, and discuss time/space complexity. Mention edge cases like empty text, empty dictionary, tokens longer than remaining text, and overlapping matches.

Key Points to Mention

  • Trie data structure for efficient longest-prefix matching
  • Time complexity: O(n * L) where n is text length and L is max token length
  • Handling of raw characters when no token matches
  • Configurable priority: store priority at terminal nodes and compare during matching
  • Trade-offs between trie and other data structures (e.g., hash set with sorted lengths)
  • Edge cases: empty input, no matches, tokens that are prefixes of others

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