Scale.ai·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Jul 2026Remote

Summary

Scale.ai SWE coding round, pretty standard data structures stuff. One question on a task scheduler implementation that was more straightforward than it sounds once you figure out the right structure.

Questions Asked (1)

Implement a TaskScheduler class with an AddTasks method that accepts a list of tasks (each with an id, deadline, and subtasks field) and a ConsumeTask method that pops and returns the task with the earliest deadline. If the scheduler is empty, return the string 'no task'.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I went straight to a min-heap keyed on the deadline field and they seemed fine with that.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and edge cases, then propose a min-heap keyed by deadline as the core data structure. Discuss the trade-offs of different implementations (e.g., heap vs. sorted list) and how to handle subtasks, ensuring the solution is efficient and scalable.

Pro tip: Mention that you would use a stable tie-breaker (like task ID or insertion order) for tasks with the same deadline to ensure deterministic behavior, and discuss how to handle dynamic updates if deadlines can change.

1. Clarify Requirements

Ask about expected input sizes, whether tasks can have the same deadline, if subtasks affect scheduling, and if tasks can be updated or removed after insertion.

2. Choose Data Structure

Propose a min-heap (priority queue) keyed by deadline for O(log n) insertion and O(log n) extraction. Discuss alternatives like a sorted list (O(n) insertion) or balanced BST.

3. Design Class Interface

Define TaskScheduler with AddTasks(list) and ConsumeTask() methods. Specify that ConsumeTask returns the task object or 'no task' if empty.

4. Handle Edge Cases

Address empty scheduler, tasks with equal deadlines (use tie-breaker), and potential concurrency if needed. Ensure 'no task' is returned appropriately.

5. Analyze Complexity

State time and space complexity: O(log n) per operation, O(n) space. Compare with other approaches to show trade-off awareness.

Key Points to Mention

Min-heap (priority queue) keyed by deadline for efficient earliest-deadline retrieval
Time complexity: O(log n) for AddTasks and ConsumeTask, O(n) space
Tie-breaking strategy for tasks with identical deadlines (e.g., by task ID or insertion order)
Handling of subtasks: whether they are independent tasks or part of the parent task
Edge case: empty scheduler returns 'no task'
Potential need for thread safety if used in concurrent environments

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