Zoox·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

Jun 2026

Summary

Zoox ML engineer interview that was basically a coding screen dressed up as a conversation. We spent most of the time on a single stock price problem, going from brute force to optimized, which felt straightforward but had a few moments where I second-guessed myself.

Questions Asked (1)

Given an array of daily stock prices, find the maximum profit from a single buy and sell transaction where the sell must happen after the buy. Return 0 if no profitable trade exists.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

Started with the double loop and they walked me through it almost too patiently, which made me wonder if I was supposed to jump straight to the optimized version.

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AI HintsAI Generated

Suggested Approach

Clarify the problem constraints (e.g., array size, price range) and confirm that a single buy-sell pair is required. Then, walk through a brute-force O(n^2) approach, followed by an optimized O(n) one-pass solution that tracks the minimum price seen so far and the maximum profit. Finally, discuss trade-offs and potential edge cases.

Pro tip: Mention that the O(n) solution is optimal for time, but if the array is extremely large and memory is constrained, you could process it as a stream, updating min price and max profit on the fly. This shows awareness of scalability, which is valued at Zoox.

1. Clarify requirements and constraints

Ask about input size, price range, and whether multiple transactions are allowed. Confirm that the sell must occur after the buy and that we return 0 if no profit is possible.

2. Discuss brute-force approach

Explain that a naive solution would check all pairs (i, j) with i < j, compute profit, and track the maximum. This is O(n^2) time and O(1) space.

3. Derive optimized one-pass solution

Describe maintaining a running minimum price and updating the maximum profit whenever the current price minus the minimum exceeds the current max profit. This yields O(n) time and O(1) space.

4. Analyze trade-offs and edge cases

Compare the brute-force and optimized approaches in terms of time and space complexity. Discuss edge cases: empty array, single element, strictly decreasing prices (profit 0), and large input.

5. Implement and test

Write clean code for the optimized solution, then walk through a few test cases to verify correctness, including the edge cases identified.

Key Points to Mention

Time and space complexity: O(n) time, O(1) space for the optimal solution.
The importance of tracking the minimum price seen so far to avoid nested loops.
Handling edge cases: empty array, single element, no profitable trade (return 0).
Trade-offs: brute-force vs. optimized approach; when O(n^2) might be acceptable (e.g., very small n).
Potential for streaming data: the one-pass solution works well for large or streaming inputs.
Relevance to ML engineering: efficient algorithms for processing large datasets, a common need in ML pipelines.

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