← Stripe Interview Insights

Stripe·Software Engineer·Onsite - Coding / Algorithms·Senior

SeniorPrefer not to say
May 2026

Summary

Stripe coding round for a software engineer role. It was a twist on the classic worker-task-assignment problem and the added complexity caught me off in a few places, but the design discussion afterward was actually pretty interesting.

Questions Asked (1)

Q1

You're given a worker-task-assignment system where each worker has a set of task types they can handle and each task has a required type. When a task comes in, assign it to the worker who supports that type and becomes available the earliest, breaking ties by worker id. How do you design this, and what's the time complexity?

Algorithms & Data StructuresSystem DesignTechnical Trade-offs
Author's notes

The base version of this problem I'd seen before, but the per-skill-type pool thing threw me off initially.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Clarify the problem constraints (e.g., number of workers, task types, frequency of assignments) and then propose a data structure that efficiently finds the earliest available worker for a given task type. Use a min-heap per task type keyed by (availability_time, worker_id) to achieve O(log n) assignment time, and discuss trade-offs with alternative approaches.

Pro tip: Mention that you would handle concurrent task arrivals with a lock or by using a thread-safe priority queue, and consider using a timestamp-based approach to avoid frequent updates to worker availability.

1. Clarify Requirements and Constraints

Ask about the scale (number of workers, task types, tasks per second), whether workers can handle multiple types, and if tasks arrive in real-time. This determines the appropriate data structures and concurrency model.

2. Design Data Structures

Propose maintaining a min-heap for each task type, where each heap stores workers who can handle that type, keyed by (availability_time, worker_id). When a worker becomes available, push them into all relevant heaps.

3. Outline Assignment Algorithm

For an incoming task of type T, pop the root from heap[T] to get the earliest available worker. If the worker is still available (check timestamp), assign the task and update the worker's availability to current_time + task_duration, then push back into all heaps they belong to.

4. Analyze Time Complexity

Assignment takes O(log n) per task type heap, where n is the number of workers for that type. If a worker supports k types, updating availability after task completion takes O(k log n). Space complexity is O(total worker-type pairs).

5. Discuss Trade-offs and Optimizations

Compare with alternatives like a global heap (O(log N) but need to filter by type) or a balanced BST. Mention lazy deletion to avoid stale entries, and consider using a hash map from worker to their current availability for quick checks.

Key Points to Mention

  • Use of min-heap per task type keyed by (availability_time, worker_id) to satisfy earliest available and tie-breaking by worker id.
  • Time complexity: O(log n) for assignment, O(k log n) for updating worker availability where k is number of task types the worker supports.
  • Handling of worker availability updates: when a task is assigned, the worker's availability time is updated and they are reinserted into heaps.
  • Lazy deletion: if a worker is popped from a heap but is no longer available (e.g., already assigned), skip and pop next.
  • Concurrency considerations: use locks or thread-safe data structures if tasks arrive concurrently.
  • Trade-offs: per-type heaps vs. global heap; memory vs. speed; handling of workers with multiple skills.

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