Google·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Google SWE phone screen with one coding problem that had a follow-up twist I never got to actually code. The core question was manageable but the time pressure was real.

Questions Asked (1)

Given a piece of text and a dictionary of token-to-ID mappings, implement a greedy longest-match tokenizer. For each position in the text, find the longest token in the dictionary that matches, output its ID, and if nothing matches, output the character literally.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

Went with a hashmap for the dictionary and a forward scan at each position, which felt right.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints (e.g., dictionary size, text length, character set) and then describe a straightforward greedy algorithm that at each position tries to match the longest possible token. Discuss how to efficiently find the longest match, such as using a trie or sorting tokens by length, and analyze time/space complexity. Finally, mention edge cases and potential trade-offs.

Pro tip: Mention that while greedy longest-match is simple, it may not always be optimal for all tokenization tasks; however, it's often used in practice for speed and simplicity. Also, consider using a trie to avoid repeatedly scanning the dictionary.

1. Clarify requirements and constraints

Ask about the size of the dictionary, the length of the text, the character set (e.g., ASCII vs Unicode), and whether the dictionary is static or dynamic. This helps determine the appropriate data structure and algorithm.

2. Design the greedy algorithm

At each position, iterate over possible token lengths from longest to shortest (or use a trie to find the longest match) and check if the substring exists in the dictionary. If a match is found, output its ID and advance by the token length; otherwise, output the character and advance by one.

3. Optimize with a trie

Build a trie from the dictionary to efficiently find the longest matching token starting at a given position. Traverse the trie character by character until no further match is possible, keeping track of the last node that corresponds to a valid token.

4. Analyze complexity and edge cases

Discuss time complexity: O(N * L) where N is text length and L is max token length, or O(N * L) with trie traversal. Space complexity: O(D) for the trie, where D is total characters in dictionary. Handle empty text, empty dictionary, and tokens that are prefixes of others.

5. Discuss trade-offs and alternatives

Compare greedy longest-match with other tokenization methods (e.g., BPE, WordPiece) and note that greedy may not always produce the optimal segmentation. Mention that for some applications, a more sophisticated approach might be needed.

Key Points to Mention

Use a trie for efficient longest-match lookup, reducing time complexity from O(N * L * D) to O(N * L).
Handle the case where no token matches by outputting the character literally and advancing by one.
Consider the impact of token overlap and ensure the algorithm correctly prioritizes longer tokens.
Analyze time and space complexity, and discuss how dictionary size and text length affect performance.
Mention edge cases: empty input, tokens that are substrings of others, and non-ASCII characters.
Discuss potential improvements or alternatives like dynamic programming for optimal segmentation, but note that greedy is often sufficient for speed.

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