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

Intermediate

Apr 2026

Summary

Scale.ai SWE interview with a multi-part coding problem involving a task scheduler. The third part asked you to add an updateDeadline method with some concurrency-aware logic.

Questions Asked (1)

Add an updateDeadline method to a task scheduler. If a task hasn't been processed yet, allow its deadline to be updated. In the consume logic, verify that the deadline and task ID still match before processing.

System DesignAlgorithms & Data StructuresTechnical Trade-offs

Author's notes

The method itself wasn't a ton of code but the tricky part was the consume side check.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the scheduler's architecture and concurrency model, then propose a data structure that supports efficient updates and lookups. Design the updateDeadline method to atomically check task status and update the deadline, and modify the consume logic to validate both deadline and task ID before processing. Discuss trade-offs between different synchronization strategies and data structures.

Pro tip: Emphasize the importance of atomicity and consistency in concurrent environments; propose using versioning or compare-and-swap to prevent race conditions between update and consume operations.

1. Clarify Requirements and Assumptions

Ask about the scheduler's concurrency model, task storage, and expected load. Confirm that tasks are processed in deadline order and that updates should only affect unprocessed tasks.

2. Design Data Structures

Choose a data structure that allows efficient lookup by task ID and ordered processing by deadline, such as a priority queue combined with a hash map. Consider how to handle updates without breaking ordering.

3. Implement updateDeadline

Ensure the method checks if the task exists and hasn't been processed, then updates the deadline atomically. Use locks or concurrent data structures to prevent race conditions.

4. Modify Consume Logic

Before processing, verify that the task's current deadline and ID match the expected values. If they don't, skip or re-queue the task to avoid processing stale entries.

5. Discuss Trade-offs and Edge Cases

Analyze performance implications of different synchronization strategies (e.g., fine-grained vs. coarse-grained locking). Address edge cases like concurrent updates, task cancellation, and duplicate entries.

Key Points to Mention

Concurrency control mechanisms (locks, atomic operations, compare-and-swap)
Data structure choices: priority queue (heap) with lazy deletion vs. balanced BST with hash map
Atomicity of check-and-update operations to prevent race conditions
Validation of task ID and deadline in consume to handle stale or updated entries
Trade-offs between update efficiency and consume efficiency
Handling of edge cases: task already processed, non-existent task, concurrent updates

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