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Bytedance·Machine Learning Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Bytedance ML engineer interview with a classic DP problem. Nothing too wild, but the LCS question is one where you either know the recurrence or you're fumbling through it live.

Questions Asked (1)

Q1

Given two strings, find the length of their longest common subsequence.

Algorithms & Data Structures
Author's notes

Classic 2D DP.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and edge cases, then explain the dynamic programming approach with a 2D table where dp[i][j] represents the LCS length of prefixes. Walk through the recurrence relation and provide time/space complexity, and if time permits, mention space optimization.

Pro tip: Emphasize the connection to sequence alignment and diff algorithms, which are relevant in ML for tasks like text similarity and DNA sequence analysis. Also, proactively discuss space optimization to show depth.

1. Clarify the problem

Confirm definitions: subsequence vs substring, whether characters are case-sensitive, and if empty strings are allowed. Ask about constraints on string lengths to determine if O(n*m) is acceptable.

2. Define the DP state and recurrence

Let dp[i][j] be the LCS length of first i characters of string A and first j characters of string B. If A[i-1] == B[j-1], dp[i][j] = dp[i-1][j-1] + 1; else dp[i][j] = max(dp[i-1][j], dp[i][j-1]).

3. Initialize and fill the table

Initialize first row and column to 0. Iterate i from 1 to n and j from 1 to m, filling dp according to the recurrence. The answer is dp[n][m].

4. Analyze complexity and optimize

Time complexity is O(n*m), space O(n*m). Mention that space can be reduced to O(min(n,m)) by keeping only the previous row, and briefly explain how.

5. Discuss applications and extensions

Relate LCS to ML: e.g., evaluating text generation, DNA sequence alignment, or diff tools. Mention possible extensions like printing the LCS or handling multiple strings.

Key Points to Mention

  • Dynamic programming approach with overlapping subproblems and optimal substructure
  • Recurrence relation: dp[i][j] = dp[i-1][j-1] + 1 if characters match, else max(dp[i-1][j], dp[i][j-1])
  • Time and space complexity: O(n*m) time, O(n*m) space, with space optimization to O(min(n,m))
  • Edge cases: empty strings, no common subsequence, identical strings
  • Applications in ML: sequence alignment, text similarity, diff algorithms
  • Difference between subsequence and substring

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