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

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May 2026Remote

Summary

Scale.ai SWE interview that was basically one meaty coding/design question about extending a task scheduling system with dependency tracking. Not a typical LeetCode grind, more of a design-meets-implementation hybrid that caught me a bit flat-footed.

Questions Asked (1)

You have a task processor that consumes tasks by earliest deadline using a min-heap. Now each task can have subtask dependencies (other task IDs that must be consumed first). Modify ConsumeTask() to respect these dependencies while still breaking ties by deadline. Also handle cycle detection.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

I started with the heap and worked outward, which was probably the right instinct.

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AI HintsAI Generated

Suggested Approach

First, clarify the problem: the min-heap orders tasks by deadline, but dependencies mean a task is only eligible when all its dependencies are consumed. Propose a solution that tracks dependency counts and dependents, uses a ready heap for eligible tasks, and detects cycles via topological sort (Kahn's algorithm). Then discuss how to integrate this with the existing ConsumeTask() method, ensuring tie-breaking by deadline and handling cycles gracefully.

Pro tip: Mention that cycle detection should happen at task addition time to fail fast, and that the ready heap must maintain the same deadline ordering as the original heap to preserve tie-breaking behavior.

1. Clarify requirements and assumptions

Confirm that dependencies are task IDs, tasks are consumed one at a time, and cycles should be detected and reported. Ask if dependencies can be added dynamically or are fixed at task creation.

2. Design data structures

Use a map from task ID to task, a map for dependency counts (in-degree), and a map for dependents (adjacency list). Maintain a min-heap of ready tasks (no unmet dependencies) ordered by deadline.

3. Modify ConsumeTask() logic

When consuming, pop from the ready heap. After consuming, decrement dependency counts of its dependents; if any become zero, push them into the ready heap. Ensure the heap orders by deadline to break ties.

4. Implement cycle detection

Use Kahn's algorithm: if the number of consumed tasks is less than total tasks after processing, a cycle exists. Alternatively, perform DFS with recursion stack during initialization. Report the cycle and handle gracefully.

5. Discuss trade-offs and edge cases

Consider performance (O(V+E) for cycle detection, O(log n) heap operations), memory overhead, and concurrency if tasks are added dynamically. Address edge cases like missing dependencies, self-dependencies, and empty heap.

Key Points to Mention

Topological sorting using Kahn's algorithm for cycle detection and dependency resolution.
Maintaining a separate ready heap to ensure tasks are only consumed when dependencies are met.
Preserving deadline-based tie-breaking by using the same comparator in the ready heap.
Handling dynamic task addition: cycle detection must be incremental or re-run.
Complexity analysis: O(V+E) for cycle detection, O(log n) per heap operation.
Graceful error handling for cycles: throw exception or return error without corrupting state.

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