Bytedance·Machine Learning Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Bytedance ML engineer interview, got a classic edit distance problem. Nothing too exotic but the DP setup took me a minute to get right under pressure.

Questions Asked (1)

Given two strings, find the minimum number of single-character operations (insertions, deletions, replacements) needed to transform one string into the other.

Algorithms & Data Structures

Author's notes

The classic edit distance problem.

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AI HintsAI Generated

Suggested Approach

Recognize this as the edit distance (Levenshtein distance) problem and propose a dynamic programming solution. Define dp[i][j] as the minimum operations to convert the first i characters of string1 to the first j characters of string2, then derive the recurrence relation based on whether the current characters match. Analyze time and space complexity, and mention potential optimizations.

Pro tip: Emphasize that this is a classic DP problem and that you can optimize space to O(min(m,n)) by using two rows. Also, relate it to real-world applications like spell checking or DNA sequence alignment to show practical understanding.

1. Clarify the problem

Confirm that operations are single-character insertions, deletions, or replacements, and that each operation costs 1. Ask if there are any constraints on string lengths or character sets.

2. Define the DP state

Let dp[i][j] be the minimum edit distance between the first i characters of string1 and the first j characters of string2. Initialize dp[0][j] = j and dp[i][0] = i.

3. Derive recurrence relation

If characters match, dp[i][j] = dp[i-1][j-1]. Else, dp[i][j] = 1 + min(dp[i-1][j] (deletion), dp[i][j-1] (insertion), dp[i-1][j-1] (replacement)).

4. Compute and return result

Fill the DP table iteratively and return dp[m][n]. Discuss time complexity O(m*n) and space complexity O(m*n), then mention space optimization to O(min(m,n)).

Key Points to Mention

Dynamic programming approach with overlapping subproblems and optimal substructure.
Recurrence relation and base cases.
Time complexity O(m*n) and space complexity O(m*n), with optimization to O(min(m,n)).
Comparison with other approaches like recursion with memoization or BFS (for small edit distances).
Real-world applications such as spell checkers, DNA sequence alignment, and plagiarism detection.
Edge cases: empty strings, identical strings, and strings of very different lengths.

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