Stripe·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Stripe technical phone screen for a software engineer role, one coding question focused on task scheduling with heaps. Pretty clean interview, just one meaty algorithmic problem and a discussion of complexity.

Questions Asked (1)

You have N workers and M tasks, each with a duration. Tasks arrive at specific times and must be assigned to the lowest-index available worker, or queued if none are free. A worker becomes free at start_time + duration. For each task, return which worker it gets assigned to.

Algorithms & Data Structures

Author's notes

The two-heap setup clicked for me pretty quickly but I fumbled the ordering on the busy heap at first.

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AI HintsAI Generated

Suggested Approach

Model the problem as a simulation with two priority queues: one min-heap for available workers keyed by worker index, and one min-heap for busy workers keyed by their free time. Process tasks in order of arrival, first releasing any workers whose free time is <= the task's arrival time, then assign the task to the smallest-index available worker or enqueue it if none are free.

Pro tip: Clarify edge cases upfront—like simultaneous task arrivals, tasks arriving before any worker is free, or multiple workers freeing at the same time—and state your assumptions. This shows thoroughness and prevents miscommunication.

1. Clarify requirements and constraints

Ask about input format, tie-breaking rules (e.g., if multiple workers free at the same time, pick lowest index), and whether tasks are processed in arrival order. Confirm output format.

2. Choose data structures

Use a min-heap for available workers (by index) and a min-heap for busy workers (by free time). This allows O(log N) operations for releasing and assigning workers.

3. Simulate task processing

Iterate through tasks sorted by arrival time. For each task, release all workers whose free time <= task arrival time into the available heap. If available heap is non-empty, pop the smallest index and assign; otherwise, add the task to a queue.

4. Handle queued tasks

After processing all arrivals, process any queued tasks in FIFO order. When a worker becomes free, assign the next queued task to the lowest-index available worker, updating the worker's free time.

5. Analyze complexity and test

State time complexity O((N+M) log N) and space O(N+M). Walk through a small example to verify correctness, including edge cases like no available workers initially.

Key Points to Mention

Use of priority queues (heaps) for efficient worker selection and release.
Tie-breaking rule: always assign to the lowest-index available worker.
Handling of queued tasks when no workers are free, ensuring FIFO order.
Time and space complexity analysis.
Edge cases: simultaneous arrivals, tasks arriving before any worker is free, multiple workers freeing at the same time.
Potential for optimizing by using a single heap or other data structures if needed.

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