Bytedance·Machine Learning Engineer·Technical Phone Screen·Intermediate

Intermediate

Apr 2026

Summary

Interviewed for an ML Engineer role at Bytedance and got a dynamic programming problem on longest common subsequence. Pretty standard algorithmic round but it still made me second-guess my approach mid-way through.

Questions Asked (1)

Given two strings, find the length of their longest common subsequence. Return 0 if none exists.

Algorithms & Data Structures

Author's notes

I knew it was a DP problem pretty quickly but then fumbled explaining the recurrence relation out loud.

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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 analyze time and space complexity, mentioning potential optimizations.

Pro tip: Mention that this DP pattern is fundamental for sequence alignment tasks in ML, such as comparing text or DNA sequences, and that space can be optimized to O(min(m,n)) using rolling arrays.

1. Clarify the problem

Confirm that subsequence means characters appear in order but not necessarily consecutively, and discuss edge cases like empty strings or no common characters.

2. Define the DP state

Let dp[i][j] be the length of LCS of first i characters of string1 and first j characters of string2. Initialize a (m+1) x (n+1) table with zeros.

3. Derive recurrence relation

If characters match, dp[i][j] = dp[i-1][j-1] + 1; else dp[i][j] = max(dp[i-1][j], dp[i][j-1]). Explain why this works.

4. Compute and return result

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

5. Discuss ML relevance

Connect LCS to ML applications like evaluating text generation, sequence alignment in bioinformatics, or diff algorithms, showing broader understanding.

Key Points to Mention

Dynamic programming approach with 2D table
Time complexity O(m*n) and space complexity O(m*n)
Space optimization using rolling array to O(min(m,n))
Recurrence relation and base cases
Edge cases: empty strings, no common subsequence
Applications in ML: sequence alignment, text similarity, diff

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