Instacart·Machine Learning Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Coding round for an ML Engineer role at Instacart. Pretty standard binary search problem but the O(log n) constraint is what makes it interesting, so if you're prepping for this company make sure your binary search fundamentals are solid.

Questions Asked (1)

Given a sorted array of integers, find the first and last positions of a target value. Return [-1, -1] if the target doesn't exist. Your solution must run in O(log n) time.

Algorithms & Data Structures

Author's notes

The naive approach is obvious and they explicitly block it with the complexity requirement.

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AI HintsAI Generated

Suggested Approach

Use two binary searches: one to find the leftmost occurrence of the target and another to find the rightmost occurrence. Modify the standard binary search to continue searching even after finding the target, adjusting the search space based on whether you're looking for the first or last position. This ensures O(log n) time complexity.

Pro tip: Clarify edge cases upfront (e.g., empty array, target not present) and mention that you'll handle them explicitly. Also, discuss how you would test the solution with examples, showing thoroughness.

1. Clarify requirements and edge cases

Confirm the input array is sorted and may contain duplicates. Discuss edge cases: empty array, target smaller than all elements, target larger than all elements, target not present.

2. Design binary search for first occurrence

Implement a modified binary search that finds the leftmost index of the target. When the target is found, record the index and continue searching in the left half to find an earlier occurrence.

3. Design binary search for last occurrence

Similarly, implement a modified binary search for the rightmost index. When the target is found, record the index and continue searching in the right half.

4. Combine results and handle absence

If either search fails to find the target, return [-1, -1]. Otherwise, return the first and last indices found.

5. Analyze complexity and test

State that both searches run in O(log n) time, so overall O(log n). Walk through a few test cases to verify correctness.

Key Points to Mention

Binary search modification: continue searching after finding target to locate boundaries.
Time complexity: O(log n) because two binary searches are performed.
Space complexity: O(1) iterative implementation.
Handling duplicates: the array may have multiple occurrences of the target.
Edge cases: empty array, target not present, single element array.
Comparison with linear scan: emphasize why O(log n) is required and how binary search achieves it.

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