Anthropic·Software Engineer·Technical Phone Screen·Senior

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Apr 2026Remote

Summary

Anthropic coding screen for a software engineer role, one question the whole time: build a tokenizer from scratch. Felt more like a systems thinking exercise than a pure coding grind.

Questions Asked (1)

Implement a tokenizer from scratch: given a vocabulary (a set of strings) and an input text, segment the text using longest-match-first. Handle characters not in the vocabulary without crashing or looping forever. Then discuss time complexity and how a trie would change things.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

My first instinct was to reach for a trie immediately, which I think actually hurt me because I spent a minute talking about it before writing anything.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem: vocabulary is a set of strings, input is text, and we need to segment using longest-match-first. Then implement a straightforward solution that scans the text, at each position tries the longest possible vocabulary word, and if none matches, handles the unknown character gracefully (e.g., emit it as a single token or skip it) to avoid infinite loops. Finally, analyze the time complexity and discuss how a trie can improve efficiency.

Pro tip: Mention edge cases like empty vocabulary, empty input, and overlapping matches; also note that longest-match-first is greedy and may not always yield the optimal segmentation, which is a good segue into discussing trade-offs.

1. Clarify requirements and constraints

Confirm the definition of vocabulary (set of strings), input text (string), and the longest-match-first rule. Ask about handling unknown characters: should they be emitted as-is, skipped, or cause an error? Also clarify if the vocabulary can contain multi-character strings and if case sensitivity matters.

2. Design the algorithm

Outline a simple algorithm: iterate over the input text with an index i. At each i, find the longest vocabulary word that matches the substring starting at i. If found, emit it and advance i by its length. If not, handle the unknown character (e.g., emit it as a single-character token) and advance i by 1 to guarantee progress.

3. Implement and test

Write code for the algorithm, ensuring no infinite loops by always advancing the index. Test with cases: normal segmentation, unknown characters, empty input, empty vocabulary, and overlapping vocabulary words.

4. Analyze time complexity

For the naive approach, at each position i, we may check up to L vocabulary words (where L is the max word length) and compare strings of length up to L, leading to O(N * L^2) worst-case time, or O(N * L * V) if checking each vocabulary word. Discuss how a trie can reduce this to O(N * L) by allowing efficient prefix matching.

5. Discuss trie optimization

Explain how a trie (prefix tree) built from the vocabulary enables matching in O(L) per position by traversing characters, and how to find the longest match by keeping track of the last terminal node encountered. This reduces the overall time to O(N * L) and is more efficient for large vocabularies.

Key Points to Mention

Longest-match-first is a greedy algorithm and may not produce the globally optimal segmentation (e.g., in cases where a shorter match enables better subsequent matches).
Handling unknown characters: emit them as single-character tokens or skip them, but always advance the index to avoid infinite loops.
Time complexity of naive approach: O(N * L * V) or O(N * L^2) depending on implementation, where N is input length, L is max vocabulary word length, and V is vocabulary size.
Trie optimization: building a trie from vocabulary allows O(L) matching per position, reducing overall time to O(N * L).
Space complexity: trie uses O(total characters in vocabulary) space, which may be large but is often acceptable.
Edge cases: empty input, empty vocabulary, vocabulary words longer than input, and overlapping matches.

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