Meta·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

May 2026

Summary

Meta coding screen for a software engineer role, just the one problem but it had enough edge cases to keep things interesting.

Questions Asked (1)

Implement a power function that computes x raised to the exponent n, handling negative exponents and doing it efficiently for large values of n.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

Knew the fast exponentiation approach going in, so I wasn't panicking.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (e.g., input types, precision, edge cases) and then present a divide-and-conquer approach using exponentiation by squaring to achieve O(log n) time. Handle negative exponents by computing the positive power and taking the reciprocal, and discuss trade-offs between iterative and recursive implementations.

Pro tip: Mention that exponentiation by squaring reduces multiplications from O(n) to O(log n), and proactively discuss how to handle integer overflow and floating-point precision issues, showing awareness of real-world constraints.

1. Clarify requirements and edge cases

Ask about input types (integer, float), expected precision, and constraints (e.g., n can be negative or zero). Identify edge cases like x=0, n=0, and negative exponents.

2. Choose an efficient algorithm

Explain exponentiation by squaring (binary exponentiation) to reduce time complexity to O(log n). Compare with naive O(n) multiplication and justify the choice.

3. Handle negative exponents and special cases

For negative n, compute the positive power and return 1/result. Handle n=0 (return 1) and x=0 with negative n (error or infinity) explicitly.

4. Implement iteratively or recursively

Write clean code, preferably iterative to avoid stack overflow for large n. Use bit manipulation or divide-and-conquer to compute the power.

5. Analyze complexity and trade-offs

State time O(log n) and space O(1) for iterative. Discuss potential overflow, precision, and alternative approaches like using built-in functions.

Key Points to Mention

Exponentiation by squaring (binary exponentiation) reduces time complexity to O(log n).
Negative exponents are handled by computing the positive power and taking the reciprocal.
Edge cases: n=0 returns 1, x=0 with negative n is undefined, and large results may overflow.
Iterative implementation avoids recursion depth issues and uses constant space.
Trade-offs: recursion is elegant but may cause stack overflow; built-in pow may be optimized but less educational.
Precision: for floating-point bases, repeated multiplication can accumulate error; consider using Math.pow for production.

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