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DoorDash·Machine Learning Engineer·Technical Phone Screen·Senior

Senior
Apr 2026

Summary

Got a coding question at DoorDash for an MLE role, basically a CPU task scheduling problem. Pretty algorithmic for an ML interview but not totally surprising for a bigger tech company.

Questions Asked (1)

Q1

You're given a list of tasks, each with an enqueue time and a processing time. A single-threaded CPU picks the available task with the shortest processing time at any given moment (ties broken by index). If nothing is available it just waits. Return the order in which tasks get processed.

Algorithms & Data Structures
Author's notes

This is a heap problem and I knew it pretty fast, but getting the edge cases right took longer than I'd like to admit.

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AI HintsAI Generated

Suggested Approach

Model the problem as a simulation using a min-heap to track available tasks by processing time, and sort tasks by enqueue time to efficiently add tasks as time progresses. Iterate through time, adding newly available tasks to the heap, then process the task with the smallest processing time (ties broken by index), updating the current time accordingly.

Pro tip: Clarify tie-breaking rules and edge cases (e.g., simultaneous enqueues, idle CPU) upfront, and discuss how the heap comparator should be defined to handle ties by index. Also, mention that the solution runs in O(n log n) time and O(n) space, which is optimal for this problem.

1. Understand the problem and constraints

Restate the problem to ensure clarity: tasks have enqueue and processing times, CPU picks shortest processing time among available tasks, ties broken by index. Identify edge cases like no tasks available initially or multiple tasks with same enqueue time.

2. Choose data structures

Use a min-heap (priority queue) to efficiently retrieve the available task with the shortest processing time, with a custom comparator that breaks ties by index. Sort the tasks by enqueue time to process them in order of availability.

3. Simulate the CPU scheduling

Initialize current time to the earliest enqueue time. While there are tasks left, add all tasks with enqueue time <= current time to the heap. If heap is empty, jump current time to the next task's enqueue time. Otherwise, pop the task with smallest processing time, record its index, and increment current time by its processing time.

4. Handle ties and edge cases

Ensure the heap comparator correctly orders by processing time and then by index. Handle the case where multiple tasks have the same enqueue time by adding them all before processing. Also, consider the scenario where the CPU is idle and must wait for the next task.

5. Analyze complexity and test

State that the time complexity is O(n log n) due to sorting and heap operations, and space complexity is O(n). Walk through a small example to verify correctness, and discuss potential optimizations or alternative approaches.

Key Points to Mention

  • Use a min-heap to efficiently select the task with the shortest processing time among available tasks.
  • Sort tasks by enqueue time to know when tasks become available.
  • Tie-breaking: when processing times are equal, the task with the smaller original index is chosen.
  • Simulation loop: maintain current time, add newly available tasks to heap, process one task, update time.
  • Handle idle CPU by advancing time to the next task's enqueue time when heap is empty.
  • Time complexity O(n log n) and space complexity O(n) due to sorting and heap usage.

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