Applovin·Backend Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Technical round at Applovin for a backend role. One coding problem, in-place constraint made it trickier than the problem itself.

Questions Asked (1)

Solve a LeetCode-style algorithmic problem (800s series) without using any extra space.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The base problem wasn't too bad but the no-extra-space constraint is what got me.

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AI HintsAI Generated

Suggested Approach

First, clarify the problem constraints and confirm what 'no extra space' means (typically O(1) auxiliary space, but input modification may be allowed). Then, identify the optimal in-place algorithm by considering techniques like two pointers, cyclic sort, or in-place hashing, and discuss trade-offs between time and space. Finally, walk through the solution step-by-step, handling edge cases and analyzing complexity.

Pro tip: Always state the time and space complexity upfront and discuss whether modifying the input is acceptable; this shows you understand practical constraints and trade-offs. Also, mention that while O(1) space is ideal, sometimes a small fixed-size array (e.g., for ASCII characters) is considered constant space.

1. Clarify constraints and assumptions

Ask if the input can be modified, what the range of values is, and whether O(1) space means no additional data structures or just no scaling with input size. Confirm the expected time complexity.

2. Identify the pattern

Recognize common in-place techniques: two pointers (for sorted arrays or partitioning), cyclic sort (for arrays with values in a known range), or in-place marking (using sign or value encoding). Relate the problem to known LeetCode patterns.

3. Design the algorithm

Outline the steps of the in-place algorithm, ensuring no extra space is used. Consider edge cases like duplicates, empty input, or negative numbers. Verify that the algorithm maintains correctness.

4. Analyze complexity and trade-offs

State the time complexity (usually O(n) or O(n log n)) and space complexity (O(1)). Discuss any trade-offs, such as modifying the input versus using extra space, and whether the solution is stable or not.

5. Implement and test

Write clean code, using descriptive variable names. Walk through a small example to demonstrate correctness, and test edge cases mentally. Be prepared to optimize further if needed.

Key Points to Mention

Definition of O(1) space: auxiliary space not scaling with input size; input modification may be allowed.
Common in-place techniques: two pointers, cyclic sort, in-place hashing (e.g., marking visited by negating values).
Time-space trade-offs: sometimes O(1) space leads to higher time complexity; discuss if acceptable.
Edge cases: empty input, single element, duplicates, negative numbers, large input.
Complexity analysis: clearly state time and space complexity, and justify why it meets the 'no extra space' requirement.
Practical considerations: in a backend context, discuss if in-place modification is safe (e.g., concurrency, immutability).

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