Zipline·Software Engineer·Onsite - Coding / Algorithms·Senior

SeniorPrefer not to say

May 2026

Summary

Post-onsite at Zipline for a SWE role, the hiring manager wasn't fully satisfied and threw in an extra LC hard to nail down my level. Not a fun surprise after thinking I was done.

Questions Asked (1)

Implement a max stack that supports push, pop, top, and retrieving the maximum element, all in O(1) time (LeetCode 716). The optimal solution was required.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

They wanted the real solution, not just a two-stack hack that passes most cases.

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AI HintsAI Generated

Suggested Approach

Use two stacks: one for the main elements and another to keep track of the maximum values. When pushing, compare the new value with the current maximum and push the larger onto the max stack. When popping, pop from both stacks to maintain consistency.

Pro tip: Discuss the trade-offs between this two-stack approach and alternatives like storing (value, max) pairs or using a doubly linked list with a tree map. Mention that the two-stack solution is optimal for O(1) time and O(n) space, but consider edge cases like popping the maximum element.

1. Clarify requirements and constraints

Confirm that all operations must be O(1) time and discuss space complexity expectations. Ask about handling duplicate maximum values and popping when empty.

2. Propose the two-stack solution

Explain how to use a main stack for elements and a max stack to track the maximum at each level. Describe push, pop, top, and peekMax operations.

3. Walk through an example

Demonstrate with a sequence of operations (e.g., push 5, push 1, push 5, pop, top, peekMax) to show how the stacks evolve and ensure correctness.

4. Analyze complexity and edge cases

State that each operation is O(1) time and O(n) space. Discuss edge cases: popping the maximum, duplicate maxima, and empty stack operations.

5. Discuss alternative approaches and trade-offs

Mention other solutions like storing pairs or using a linked list with a balanced BST, and compare their time/space trade-offs.

Key Points to Mention

Two stacks: one for elements, one for maximums
Push: compare new value with current max, push larger onto max stack
Pop: pop from both stacks to maintain max tracking
Time complexity: O(1) for all operations
Space complexity: O(n) due to auxiliary stack
Handling duplicates: push equal max onto max stack to handle multiple occurrences

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