Anthropic·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

May 2026Remote

Summary

Coding round at Anthropic for a software engineer role. One question, but it had enough layers to keep me busy for a while. The complexity discussion at the end was where things got interesting.

Questions Asked (1)

Given a string and a dictionary of tokens, tokenize the string using a longest-match greedy rule: at each position, consume the longest matching token, advance, and repeat. Return the token sequence or signal failure if none exists. Then discuss the naive vs trie-based approach, their time complexities, and whether anything other than a trie can match the trie's worst-case performance.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

Started with the naive scan and got it working fine.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and walking through a concrete example to demonstrate the greedy longest-match rule. Then present a naive solution, analyze its complexity, and introduce a trie-based optimization. Finally, discuss alternative data structures and their trade-offs, emphasizing the trie's worst-case guarantees.

Pro tip: Mention that the greedy approach may fail even when a valid tokenization exists, and briefly discuss how backtracking or dynamic programming could handle such cases—this shows depth beyond the immediate question.

1. Clarify and Example

Restate the problem in your own words and walk through a small example to confirm understanding of the longest-match greedy rule and failure condition.

2. Naive Approach

Describe the straightforward method: at each position, check every token in the dictionary to find the longest match. Analyze its time complexity (O(n * m * L) where n is string length, m is number of tokens, L is max token length).

3. Trie-Based Optimization

Explain how to build a trie from the dictionary and traverse it from each position to find the longest match efficiently. Analyze the improved time complexity (O(n * L) for traversal, plus O(total token length) for trie construction).

4. Compare and Discuss Alternatives

Compare naive vs trie approaches, highlighting the trie's advantage in worst-case scenarios. Discuss whether other structures (e.g., hash sets with length checks, Aho-Corasick, suffix automata) can match the trie's worst-case performance, noting trade-offs.

5. Edge Cases and Limitations

Mention edge cases (empty string, no match, overlapping tokens) and the limitation of greedy matching (may fail when a valid tokenization exists). Briefly note how to handle failure (e.g., return null or throw exception).

Key Points to Mention

Greedy longest-match may not find a valid tokenization even if one exists; it's not backtracking.
Naive approach time complexity: O(n * m * L) where n is string length, m is number of tokens, L is max token length.
Trie-based approach time complexity: O(n * L) for matching, plus O(total token length) for trie construction.
Trie provides worst-case O(n * L) matching, which is optimal for this problem.
Alternative structures like hash sets with length checks can achieve similar average-case but may degrade in worst-case; Aho-Corasick is for multiple pattern matching but not directly for longest-match at each position.
Space complexity: trie uses O(total token length) space, which is efficient for large dictionaries.

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