Roblox·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Apr 2026

Summary

Roblox software engineer interview with a randomized partitioning problem that sounds deceptively simple but has a few layers to it once you get into the complexity and testing discussion.

Questions Asked (1)

Given a list of integers and an integer k, write a function that randomly partitions the list into k non-empty segments and returns a list of lists. Walk through your approach, analyze the time and space complexity, and explain how you'd verify that the result is actually random.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The coding part wasn't too bad.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem: ensure k <= n and segments are non-empty. Then, choose k-1 distinct cut points from the n-1 possible gaps between elements, which can be done by randomly selecting k-1 gaps without replacement. Finally, slice the list at these cut points to form the segments, and discuss complexity and randomness verification.

Pro tip: Mention that using a uniform random selection of cut points ensures each partition is equally likely, and that you can verify randomness by running the function many times and checking that the distribution of cut points is approximately uniform.

1. Clarify requirements and edge cases

Confirm that k must be between 1 and the list length, and that segments must be non-empty. Discuss what to do if k > n (e.g., return empty list or throw error).

2. Choose a random partitioning strategy

Decide to select k-1 distinct cut points from the n-1 possible gaps between elements. This ensures non-empty segments and uniform randomness if done correctly.

3. Implement the selection and slicing

Use a random sampling method (e.g., Fisher-Yates shuffle on gap indices, then sort the first k-1) to pick cut points. Then slice the original list at these indices to build the segments.

4. Analyze time and space complexity

Time: O(n) for sampling and slicing (or O(k) if using reservoir sampling). Space: O(n) for the output, plus O(k) for cut points. Discuss trade-offs.

5. Explain randomness verification

Describe how to test uniformity: run the function many times, record the positions of cuts, and use a chi-squared test or visual inspection to check that each possible set of cut points appears with roughly equal frequency.

Key Points to Mention

Uniformity: each partition of the list into k non-empty segments should be equally likely.
Efficiency: selecting cut points can be done in O(k) time using reservoir sampling or O(n) with a partial shuffle.
Edge cases: k=1 (no cuts), k=n (each element its own segment), k>n (invalid).
Implementation details: using random.sample on range(n-1) to pick k-1 distinct gaps, then sorting.
Complexity: time O(n) for slicing, space O(n) for output; if k is small, O(k) time for selection.
Verification: statistical tests (e.g., chi-squared) on the distribution of cut points or segment lengths.

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