Max Stack
A hard-tier problem at 46% community acceptance, tagged with Linked List, Stack, Design. Reported in interviews at LinkedIn and 3 others.
Max Stack shows up in system-design rounds at LinkedIn, Walmart Labs, Lyft, and Snap, and it's deceptively hard. You need to build a stack that supports push, pop, and instant retrieval of the maximum element. The naive approach tanks on the max lookup, and the obvious fix (store max at every node) falls apart when you pop. With a 45% acceptance rate, this is a real gut-check. If you freeze on the trick during a live assessment, StealthCoder surfaces the working design invisible to the proctor.
Companies that ask "Max Stack"
Max Stack is the kind of problem that decides whether you pass. StealthCoder reads the problem on screen and surfaces a working solution in under 2 seconds. Invisible to screen share. The proctor sees nothing. Built by a senior engineer who knows the OA is theater. This is the script.
Get StealthCoderThe key insight is that you can't just track one global max. You need to pair each value with the max seen up to that point, then use a doubly-linked list and ordered set to handle deletions without scanning the whole stack. When you pop a value, you can't naively search for the next-largest max. Instead, maintain a doubly-linked list of max candidates with a supporting ordered set (or heap). The pattern breaks down fast if you try to optimize space or use a single auxiliary stack. The real difficulty isn't the data structure complexity, it's realizing that pop() must be O(log n) at worst, not O(n). StealthCoder handles the full implementation and edge cases in real time if this problem appears during your live OA.
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Max Stack recycles across companies for a reason. It's hard-tier, and most candidates blank under the timer. StealthCoder is the hedge: an AI overlay invisible during screen share. It reads the problem and surfaces a working solution in under 2 seconds. Built by a senior engineer who knows the OA is theater. This is the script. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Max Stack interview FAQ
How is Max Stack different from LeetCode Min Stack?+
Min Stack is easier because you only need one max per state. Max Stack demands you track the entire history of maxima as you pop, so you can't throw away old max values. You need a data structure that supports fast insertion, deletion, and retrieval of the second-largest max when the current max is popped. That's why the problem jumps to Hard.
Why does a simple auxiliary stack fail here?+
A single aux stack stores the max at each step, but it only works if you never remove elements from the middle. Since this design problem sometimes includes a delete or remove operation by value, not just pop, you need a set to track and remove old max values. A linked list without indexing is too slow for lookups.
Is Max Stack still asked at these companies?+
Yes. LinkedIn, Walmart Labs, Lyft, and Snap all report it. It shows up most often in systems or backend interview loops where candidates design a service that needs fast max queries. It's less common in pure coding rounds, but still a real test of data-structure intuition.
What's the time complexity I should aim for?+
Push and pop should both be O(log n). If your solution is O(n) for either operation, you're missing the ordered set or heap component. The problem is testing whether you can avoid a full scan when the current max is removed.
Should I use a heap or a balanced BST for the max candidates?+
A balanced BST (or ordered set like TreeMap in Java) is safer because you need to delete arbitrary elements when you pop. A heap doesn't support efficient deletion of non-root elements. Linked list with binary search is also workable, but ordered set is the clearest design.
Want the actual problem statement? View "Max Stack" on LeetCode →