Meta·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Apr 2026

Summary

Meta SWE coding round, pretty much just one problem but it's the kind that trips you up if you're not careful about edge cases.

Questions Asked (1)

Implement a power function (raising a number to an exponent) from scratch, without using any built-in power or exponentiation utilities.

Algorithms & Data Structures

Author's notes

Seemed easy at first and I started writing the naive loop version.

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Suggested Approach

Start by clarifying the problem constraints (integer vs. floating-point inputs, negative exponents, edge cases) and then present a brute-force O(n) solution before optimizing to O(log n) using exponentiation by squaring. Discuss handling of negative exponents, zero base, and potential overflow, and analyze time and space complexity.

Pro tip: Mention that exponentiation by squaring is the standard approach and that you can handle negative exponents by computing the positive power and then taking the reciprocal. Also, proactively discuss edge cases like 0^0 and overflow, showing attention to detail.

1. Clarify requirements and edge cases

Ask about input types (integer, float), exponent range (negative, zero), and expected output precision. Identify edge cases such as base 0, exponent 0, and negative exponents.

2. Propose a brute-force solution

Describe a simple loop that multiplies the base exponent times. Analyze its O(n) time complexity and note it's inefficient for large exponents.

3. Optimize with exponentiation by squaring

Explain the divide-and-conquer approach: recursively compute base^(n/2), square it, and multiply by base if n is odd. This reduces time complexity to O(log n).

4. Handle negative exponents and edge cases

For negative exponents, compute the positive power and return its reciprocal. Handle base 0 and exponent 0 appropriately (e.g., 0^0 = 1 by convention).

5. Analyze complexity and discuss optimizations

State time complexity O(log n) and space complexity O(log n) for recursion (or O(1) for iterative). Mention potential overflow and how to mitigate (e.g., using floats or modular arithmetic if applicable).

Key Points to Mention

Exponentiation by squaring (binary exponentiation) reduces time complexity from O(n) to O(log n).
Handling negative exponents by computing the positive power and taking the reciprocal.
Edge cases: base 0, exponent 0, negative base with fractional exponent (if floats allowed).
Time and space complexity analysis: O(log n) time, O(log n) space for recursion, O(1) for iterative.
Potential overflow issues with large results and how to handle them (e.g., using floating-point or modular arithmetic).
Iterative vs. recursive implementation trade-offs.

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