Pinterest·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

Jun 2026

Summary

Pinterest MLE interview with a coding question around prefix search on a sorted word list. Pretty algorithmic for an ML role, but not shocking given how Pinterest structures their loops.

Questions Asked (1)

Given a lexicographically sorted list of words and a list of prefix queries, return for each query the index of the first word that starts with that prefix, or -1 if no such word exists.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

My first instinct was trie, which felt clean for batched queries.

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AI HintsAI Generated

Suggested Approach

Clarify the problem constraints (list size, number of queries, word lengths) and discuss trade-offs between preprocessing and query time. Propose a binary search solution on the sorted list for each query, leveraging the lexicographic order to find the first word with the given prefix. Optionally, mention a trie-based approach for scenarios with many queries or dynamic updates.

Pro tip: Emphasize that since the list is already sorted, binary search is optimal for single queries, but if the number of queries is huge, building a trie or using a prefix hash map can reduce per-query time to O(1) at the cost of memory. Discussing these trade-offs shows engineering maturity.

1. Clarify constraints and requirements

Ask about the size of the word list, number of queries, maximum word length, and whether the list can be modified. This determines the best approach.

2. Choose the right data structure

For a static sorted list, binary search is efficient. For many queries or dynamic updates, consider a trie or prefix hash map.

3. Design the algorithm

For binary search, define a custom comparison that checks if a word starts with the prefix. Find the leftmost index where the word is >= prefix and verify it starts with the prefix.

4. Analyze time and space complexity

Binary search per query: O(L log N) time, O(1) space. Trie: O(L) per query, O(total characters) space. Discuss trade-offs.

5. Handle edge cases and test

Consider empty prefix, no matching words, prefix longer than any word, and duplicate words. Walk through examples.

Key Points to Mention

Binary search on sorted list using prefix comparison
Time complexity: O(L log N) per query, where L is prefix length
Alternative: Trie for O(L) per query but higher memory
Edge cases: empty prefix, no match, prefix longer than words
Trade-offs between preprocessing and query time
Use of built-in functions like lower_bound in C++ or bisect in Python

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