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Google·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Jun 2026

Summary

Got a coding problem at Google for a SWE role that was basically a custom tokenizer using greedy longest-match against a dictionary. Felt like something you'd see in an NLP pipeline, which was a fun twist compared to the usual array/graph stuff.

Questions Asked (1)

Q1

Given a text string and a dictionary mapping tokens to integer IDs, process the string left to right: at each position, find the longest dictionary token that matches the current prefix, replace it with its ID in the output, and advance past it. If nothing matches, copy the character as-is and move one step forward. Return the final output string.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

The greedy part clicked pretty fast but I initially forgot to handle the 'no match' case properly and was just skipping characters instead of appending them.

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AI HintsAI Generated

Suggested Approach

Clarify the problem constraints and edge cases, then propose a greedy left-to-right algorithm using a trie for efficient longest-prefix matching. Discuss time/space complexity and potential optimizations, and walk through an example to validate correctness.

Pro tip: Mention that a trie can be built once and reused for multiple strings, and that if the dictionary is small, a simple loop over tokens sorted by length might be sufficient—showing you consider trade-offs.

1. Clarify requirements and edge cases

Ask about input size, dictionary size, token length limits, and expected output format. Discuss handling of overlapping tokens, empty strings, and non-matching characters.

2. Design the algorithm

Propose a greedy left-to-right scan. At each position, find the longest dictionary token that matches the current prefix. If found, append its ID and advance by token length; else append the character and advance by one.

3. Choose data structures for efficiency

Use a trie (prefix tree) to store dictionary tokens for O(L) lookup per position, where L is the max token length. Alternatively, if the dictionary is small, sort tokens by length and check each.

4. Analyze complexity and trade-offs

Time: O(N * L) with trie, where N is string length and L is max token length. Space: O(D * L) for trie, D = number of tokens. Discuss trade-offs vs. naive approach.

5. Test with examples and edge cases

Walk through a sample string, including cases with overlapping tokens, no matches, and tokens at the end. Verify correctness and performance.

Key Points to Mention

  • Greedy left-to-right processing ensures longest match at each position.
  • Trie enables efficient longest-prefix matching in O(L) per position.
  • Time complexity: O(N * L) with trie, O(N * D * L) with naive token list.
  • Space complexity: O(D * L) for trie, O(D) for token list.
  • Edge cases: empty string, no matches, tokens that are prefixes of others, overlapping tokens.
  • Trade-offs: trie vs. sorted token list based on dictionary size and query frequency.

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