Bytedance·Machine Learning Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Bytedance ML engineer screen, pretty chill. Coding was straightforward and the interviewer was genuinely helpful when things got tricky.

Questions Asked (1)

Given an array of stock prices, find the maximum profit you can make from a single buy and sell transaction.

Algorithms & Data Structures

Author's notes

Classic problem, got through it fine.

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AI HintsAI Generated

Suggested Approach

Clarify the problem constraints (e.g., single transaction, cannot sell before buying) and then present an O(n) one-pass solution that tracks the minimum price seen so far and the maximum profit. Walk through a small example to validate the logic and discuss edge cases like decreasing prices.

Pro tip: Explicitly state that you're optimizing for time and space complexity (O(n) time, O(1) space) and mention that this is a classic problem often used to assess algorithmic thinking. Also, relate it to real-world scenarios like trading or feature engineering in ML pipelines to show domain awareness.

1. Clarify the problem

Confirm that only one buy and one sell are allowed, and that the sell must occur after the buy. Ask about edge cases such as empty array or single element.

2. Discuss brute force and optimize

Mention the O(n^2) brute force approach of checking all pairs, then explain how to optimize to O(n) by tracking the minimum price and maximum profit in one pass.

3. Present the algorithm

Initialize min_price to infinity and max_profit to 0. Iterate through prices, update min_price if current price is lower, else update max_profit if current price - min_price is greater.

4. Walk through an example

Use a sample array like [7,1,5,3,6,4] to demonstrate how the algorithm works step by step, showing the updates to min_price and max_profit.

5. Analyze complexity and edge cases

State that time complexity is O(n) and space is O(1). Discuss edge cases: empty array returns 0, decreasing prices yield 0 profit, and large arrays are handled efficiently.

Key Points to Mention

Single transaction constraint: buy once, sell once, sell after buy.
O(n) time and O(1) space solution using one pass.
Tracking minimum price and maximum profit.
Handling edge cases: empty array, single element, strictly decreasing prices.
Comparison with brute force O(n^2) approach.
Potential extension to multiple transactions (if asked) or relation to ML feature engineering.

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