LinkedIn·Software Engineer·Onsite - Coding / Algorithms·Intermediate

Intermediate

Jun 2026

Summary

LinkedIn software engineer interview that leaned hard into complexity analysis. Not just 'what's the Big-O' but justify it, compare alternatives, talk trade-offs. More rigorous than I expected.

Questions Asked (1)

For an algorithm you implement, analyze its time and space complexity in full: derive best, average, and worst cases, identify the dominant operations, explain how it scales with input size, compare at least one alternative approach with trade-offs, and describe any optimizations you'd apply.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

This is way more than the usual 'state the Big-O and move on.' They wanted me to actually derive it and justify each case separately, not just say O(n log n) and shrug.

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AI HintsAI Generated

Suggested Approach

Choose a concrete algorithm you know well, state its purpose and input/output, then systematically derive time and space complexity for best, average, and worst cases. Identify dominant operations, explain scaling, compare with an alternative approach, and finish with practical optimizations.

Pro tip: Tie complexity analysis to real-world impact: mention how the algorithm would perform at LinkedIn's scale (e.g., millions of users) and when the alternative might be preferable despite worse asymptotic complexity.

1. Define the algorithm and input

Briefly describe the algorithm, its purpose, and the input size parameter (e.g., n). Clarify assumptions about input distribution and constraints.

2. Analyze time complexity

Derive best, average, and worst-case time complexities by counting dominant operations (e.g., comparisons, swaps, memory accesses). Explain how each case arises.

3. Analyze space complexity

Determine auxiliary space usage (in-place vs. extra data structures) and total space. Discuss how space scales with input size.

4. Compare with an alternative

Select a competing algorithm, outline its time/space complexity, and discuss trade-offs (e.g., faster average case but higher memory, or better worst-case but more complex).

5. Propose optimizations

Suggest practical optimizations (e.g., early termination, hybrid approaches, caching, parallelization) and explain their impact on complexity and real-world performance.

Key Points to Mention

Best, average, and worst-case time complexity with clear derivations
Dominant operations (e.g., comparisons, swaps, recursive calls) and their counts
Space complexity: auxiliary vs. total, and in-place vs. out-of-place
Scaling behavior: how runtime and memory grow with input size (e.g., O(n log n) vs. O(n^2))
Alternative approach with trade-offs (e.g., quicksort vs. mergesort: average speed vs. worst-case guarantee)
Optimizations (e.g., choosing a better pivot, using insertion sort for small arrays, tail recursion elimination)

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