Zoox·Machine Learning Engineer·Technical Phone Screen·Senior

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Apr 2026

Summary

Zoox ML engineer interview that went deeper than I expected. Started as a standard coding question and turned into a full concurrency design problem. The skeleton they gave you was both helpful and slightly disorienting because you had to figure out what they actually wanted filled in.

Questions Asked (1)

You know the classic single buy/sell stock profit problem. Now imagine the prices array is massive. How would you parallelize the solution across K contiguous chunks, and how do you merge the results correctly at the end?

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

The per-chunk local computation part I got fine, min price, max price, best profit within the chunk.

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AI HintsAI Generated

Suggested Approach

Explain that the single buy/sell stock profit problem can be parallelized by dividing the array into K contiguous chunks, computing local min price, max profit, and max price for each chunk, then merging these summaries sequentially to account for cross-chunk transactions. Emphasize that the merge step must consider the minimum price from earlier chunks and the maximum price from later chunks to capture the best buy-sell pair across boundaries.

Pro tip: Mention that the merge step can be done in O(K) time and that the overall parallel algorithm achieves O(N/K + K) time with O(K) extra space, which is efficient for massive arrays. Also, note that if K is chosen as sqrt(N), the total work is minimized.

1. Divide the array into K contiguous chunks

Split the prices array into K roughly equal-sized contiguous segments, ensuring each chunk is processed independently.

2. Compute local summaries per chunk

For each chunk, compute three values: the minimum price, the maximum profit (max difference where buy before sell within the chunk), and the maximum price.

3. Merge summaries sequentially

Iterate through chunks in order, maintaining the global minimum price seen so far and updating the global maximum profit by considering the profit from buying at the global minimum and selling at the current chunk's maximum price, as well as the chunk's local max profit.

4. Return the global maximum profit

After processing all chunks, the global maximum profit is the answer, correctly accounting for both intra-chunk and cross-chunk transactions.

Key Points to Mention

The need to track both minimum price and maximum profit per chunk to handle cross-chunk transactions.
The merge step must consider the minimum price from all previous chunks and the maximum price from the current chunk to compute cross-chunk profit.
The algorithm is embarrassingly parallel for the local computations, with a sequential merge of O(K) time.
Choosing K optimally (e.g., sqrt(N)) balances parallel overhead and merge cost.
Handling edge cases such as decreasing prices (profit zero) and ensuring at least one buy-sell pair.
The approach generalizes to other problems like maximum subarray sum with similar divide-and-conquer parallelization.

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