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Bytedance·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Bytedance software engineer interview with a word search problem that had a pretty nasty optimization twist. The core question was straightforward but the constraint about 30,000 dictionary words made it a real trie problem, not just a DFS.

Questions Asked (1)

Q1

Given an m x n grid of letters and a large list of dictionary words, find all words from the list that can be formed by tracing a path through adjacent cells (up, down, left, right) without reusing any cell. Optimize for a very large dictionary rather than searching independently for each word.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

My first instinct was to just do DFS for each word separately.

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AI HintsAI Generated

Suggested Approach

Build a trie from the dictionary words to enable efficient prefix pruning during a single DFS traversal of the grid. For each cell, perform DFS while simultaneously walking the trie, marking cells as visited to avoid reuse, and collecting any complete words found. This avoids redundant searches for each word and handles a large dictionary efficiently.

Pro tip: Mention that you can optimize memory and speed by storing words at trie nodes and pruning nodes after they are found if duplicates are not needed, and discuss trade-offs between trie and hash set approaches.

1. Clarify requirements and constraints

Ask about grid size, dictionary size, whether words can be reused, and if output should be unique. This helps choose the right data structures and algorithms.

2. Design trie for dictionary

Insert all dictionary words into a trie, storing the complete word at terminal nodes. This allows prefix-based pruning during search.

3. Perform DFS with backtracking

For each cell, start a DFS that moves in four directions, checking if the current path is a prefix in the trie. Mark cells as visited and unmark on backtrack.

4. Collect and deduplicate results

When a terminal node is reached, add the word to the result set. Optionally, remove the word from the trie to avoid duplicates and prune further.

5. Analyze complexity and optimizations

Discuss time complexity O(m*n*4^L) worst-case but pruned by trie, and space O(total characters in dictionary). Mention possible optimizations like early termination.

Key Points to Mention

  • Trie data structure for efficient prefix matching
  • Depth-first search with backtracking and visited cell tracking
  • Pruning: stop DFS when current path is not a prefix in trie
  • Handling duplicates: using a set or removing words from trie after found
  • Time and space complexity analysis
  • Trade-offs: trie vs. hash set, memory vs. speed, and potential for parallelization

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