TikTok·Machine Learning Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

TikTok ML Engineer interview with a dynamic programming problem. Pretty standard coding round but the constraints were tight enough that a naive solution would've timed out, so you had to think about efficiency from the start.

Questions Asked (1)

Given a string and a dictionary of words, determine whether the string can be fully broken down into a sequence of valid dictionary words. Words can be reused.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

My first instinct was recursion, which technically works but blows up on longer inputs.

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AI HintsAI Generated

Suggested Approach

Clarify the problem constraints (string length, dictionary size, word lengths) and edge cases, then propose a dynamic programming solution where dp[i] indicates if the first i characters can be segmented. Discuss time/space complexity and possible optimizations like using a trie or BFS, and relate it to real-world ML applications such as tokenization.

Pro tip: Mention that this problem is analogous to word segmentation in NLP tokenizers, and highlight the trade-off between DP and BFS with memoization; also note that using a trie can reduce lookup time from O(n) to O(L) where L is max word length.

1. Clarify requirements and edge cases

Ask about input constraints (string length, dictionary size, word length limits), whether the dictionary can contain duplicates, and if the empty string is considered breakable. Confirm that words can be reused.

2. Propose a dynamic programming solution

Define dp[i] as whether the substring s[0:i] can be segmented. Initialize dp[0] = true, then for each i from 1 to n, check all j < i where dp[j] is true and s[j:i] is in the dictionary. Return dp[n].

3. Analyze complexity and optimize

The naive DP is O(n^2 * L) where L is average word length due to substring hashing. Optimize by using a trie for dictionary lookups, reducing to O(n * L_max) or using BFS with memoization to avoid redundant checks.

4. Discuss alternative approaches and trade-offs

Compare DP with BFS/DFS + memoization, and mention that BFS can find the minimum number of words if needed. Also note that if the dictionary is large, a trie or hash set is preferable.

5. Relate to ML engineering context

Connect the problem to tokenization in NLP models (e.g., WordPiece, BPE) and discuss how efficient word segmentation impacts model inference speed and memory usage.

Key Points to Mention

Dynamic programming with dp array and recurrence relation
Time and space complexity analysis (O(n^2) vs O(n * L_max) with trie)
Edge cases: empty string, single character, no valid segmentation
Optimization using trie or hash set for O(1) average word lookup
BFS/DFS with memoization as alternative, and ability to reconstruct segmentation
Connection to NLP tokenization and ML engineering trade-offs

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