Instacart·Machine Learning Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Interviewed for an MLE role at Instacart, got a pretty standard coding question but with a twist that I almost missed entirely.

Questions Asked (1)

Given a sorted array of integers, return a new array of the squared values, also in sorted order. Then optimize it to run in linear time.

Algorithms & Data Structures

Author's notes

My first instinct was just square everything and sort it, which works but they pushed back immediately asking if I could do better.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and discussing a naive O(n log n) solution using squaring and sorting. Then derive the optimal O(n) two-pointer approach that leverages the sorted order and the fact that squares of negative numbers can be large. Walk through the algorithm with an example, analyze complexity, and discuss edge cases.

Pro tip: Explicitly connect the algorithm to a real-world ML scenario, such as efficiently processing sorted feature vectors or embeddings, to show you think beyond the coding problem. Also, mention that the two-pointer technique is a common pattern in ML pipelines for merging sorted data.

1. Clarify and Confirm

Restate the problem, confirm input/output types, and ask about edge cases like empty array, duplicates, and negative numbers. This ensures you understand the requirements before coding.

2. Naive Solution

Propose the straightforward approach: square each element and sort the result. Analyze its time complexity as O(n log n) and note that it doesn't leverage the sorted input.

3. Optimal Two-Pointer Approach

Explain that the largest squares come from either end of the sorted array. Use two pointers starting at both ends, compare absolute values, and fill the result array from the end to the beginning in O(n) time.

4. Walk Through Example

Trace the algorithm on a sample array like [-4, -2, 0, 1, 3] to demonstrate correctness and help the interviewer follow your logic.

5. Complexity and Edge Cases

State time and space complexity (O(n) time, O(n) space for output). Discuss handling of empty arrays, all negatives, all positives, and duplicates.

Key Points to Mention

Time complexity: O(n) for the optimal solution vs O(n log n) for naive.
Space complexity: O(n) for the output array; O(1) extra space if output is not counted.
Two-pointer technique: left and right pointers moving inward based on absolute values.
Why the naive approach is suboptimal: it ignores the sorted property of the input.
Edge cases: empty array, single element, all negative, all positive, zeros, duplicates.
Real-world relevance: efficient processing of sorted data in ML pipelines (e.g., feature engineering, similarity search).

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