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Eightfold AI·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Interviewed for a software engineering role at Eightfold AI and got hit with a binary search problem that looks deceptively easy until you actually think about the O(log n) constraint.

Questions Asked (1)

Q1

Given an integer array, find the index of any peak element, where a peak is strictly greater than its immediate neighbors. Your solution must run in O(log n) time.

Algorithms & Data Structures
Author's notes

My first instinct was a linear scan and I almost said it out loud before catching myself.

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AI HintsAI Generated

Suggested Approach

Use a modified binary search to find a peak element in O(log n) time. At each step, compare the middle element with its neighbors and move towards the side with the larger neighbor, ensuring a peak is found.

Pro tip: Clarify edge cases upfront: what if the array has multiple peaks? Any peak is acceptable. Also, handle arrays of length 1 and boundary conditions where peak can be at the ends.

1. Clarify requirements and edge cases

Confirm that any peak is acceptable, and discuss edge cases like array length 1, peaks at boundaries, and duplicate elements (though problem says strictly greater).

2. Explain the binary search approach

Describe how to use binary search: compare mid with its neighbors, and decide to go left or right based on which neighbor is larger.

3. Walk through an example

Trace the algorithm on a sample array to demonstrate correctness and O(log n) time complexity.

4. Discuss complexity and potential pitfalls

State time complexity O(log n) and space O(1). Mention pitfalls like infinite loops if not careful with mid updates.

5. Write pseudocode or code

Provide clean pseudocode or actual code, handling boundaries by treating out-of-bounds as negative infinity.

Key Points to Mention

  • Binary search variation for peak finding
  • Time complexity O(log n) and space O(1)
  • Handling boundaries by treating out-of-bounds as -∞
  • Proof of correctness: why moving towards larger neighbor guarantees a peak
  • Edge cases: single element, peak at ends, multiple peaks
  • Comparison with linear scan O(n) and why binary search is better

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