Weride·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Apr 2026

Summary

Weride SWE interview hit me with the classic kth largest element problem. They wanted more than just 'sort and index', so be ready to actually code quickselect and defend your pivot choices.

Questions Asked (1)

Given an integer array and an integer k, find the k-th largest element in the array. Walk through multiple approaches and their trade-offs, then implement a partition-based selection algorithm from scratch.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I started with sorting because it's the easiest to reason about, but they pushed back pretty fast asking if I could do better.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints (e.g., array size, duplicates, k validity) and then present multiple approaches: sorting, min-heap, and quickselect. Compare their time/space complexities and trade-offs, then implement the partition-based quickselect algorithm from scratch, explaining each step and handling edge cases.

Pro tip: Mention that quickselect has O(n) average time but O(n^2) worst-case, and that randomizing the pivot or using median-of-medians can guarantee O(n) worst-case. Also, note that for small k, a min-heap of size k is often more practical in real systems due to predictable performance and streaming capability.

1. Clarify requirements and constraints

Ask about input size, range of values, duplicates, and whether k is guaranteed valid. Confirm if the array can be modified and if additional space is allowed.

2. Discuss multiple approaches

Outline sorting (O(n log n)), min-heap of size k (O(n log k)), and quickselect (O(n) average). Compare their time/space complexities and suitability for different scenarios.

3. Explain quickselect algorithm

Describe the partition-based selection: choose a pivot, partition the array, and recursively search only the side containing the k-th largest. Emphasize average O(n) time and in-place nature.

4. Implement partition and quickselect

Write code for the partition function (e.g., Lomuto or Hoare) and the recursive/iterative quickselect. Handle edge cases like k=1, k=n, and duplicates.

5. Analyze complexity and trade-offs

Summarize time/space complexity, worst-case scenarios, and optimizations (random pivot, median-of-medians). Discuss when to prefer heap or sorting.

Key Points to Mention

Time and space complexity of each approach: sorting O(n log n), heap O(n log k), quickselect O(n) average.
Quickselect partition logic: rearranging elements around a pivot so that elements greater than pivot are on one side.
Handling duplicates: ensure partition correctly places equal elements and that k-th largest is defined (e.g., k-th in sorted order).
Worst-case O(n^2) of quickselect and mitigation via random pivot or median-of-medians.
In-place nature of quickselect vs. extra space for heap or sorting.
Edge cases: k=1 (maximum), k=n (minimum), empty array, invalid k.

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