Meta·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Meta SWE interview where I had to take a suboptimal solution I'd already proposed and figure out how to fix it on the fly, covering edge cases and cleaning up the implementation under time pressure. Not a great feeling.

Questions Asked (1)

You've proposed a suboptimal solution to an algorithm problem. Given time constraints, how would you optimize it to handle all edge cases and improve the implementation? Assume input size is around 10,000 elements.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The rough part wasn't identifying that my solution was slow, it was having to articulate the fix out loud while also patching the code in real time.

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AI HintsAI Generated

Suggested Approach

Acknowledge the suboptimal solution, then systematically analyze its time and space complexity to identify bottlenecks. Prioritize optimizations based on the 10,000-element input size, ensuring all edge cases are handled, and propose a concrete improved implementation with trade-offs.

Pro tip: Demonstrate awareness of Meta's scale by discussing how your solution would perform if input size grew to millions, showing foresight beyond the immediate problem.

1. Analyze Current Solution

Evaluate the time and space complexity of the proposed solution, identifying specific inefficiencies and potential edge cases that are not handled.

2. Prioritize Optimizations

Given the input size of 10,000, determine which optimizations yield the most significant performance gains, focusing on algorithmic improvements over micro-optimizations.

3. Handle Edge Cases

Enumerate all possible edge cases (e.g., empty input, duplicates, extreme values) and ensure the optimized solution addresses them without compromising performance.

4. Implement and Validate

Write clean, efficient code for the optimized solution, then validate with test cases including edge cases and performance benchmarks.

5. Discuss Trade-offs

Explain the trade-offs made (e.g., time vs. space, readability vs. performance) and justify why the chosen optimizations are appropriate for the given constraints.

Key Points to Mention

Time and space complexity analysis (Big O notation) of both original and optimized solutions
Specific data structures or algorithms that improve efficiency (e.g., hash maps, sorting, two-pointer technique)
Edge cases: empty input, single element, duplicates, sorted/reverse-sorted input, large values
Input size consideration: 10,000 elements is small enough for O(n log n) but not for O(n^2)
Trade-offs between different optimization strategies (e.g., pre-sorting vs. on-the-fly processing)
Testing methodology: unit tests, performance profiling, and scalability considerations

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