Openai·Software Engineer·Technical Phone Screen·Senior

Senior

May 2026

Summary

OpenAI SWE interview with a meaty system design coding problem around task scheduling. The problem had a lot of moving parts and the fairness/balancing angle made it trickier than a standard scheduler question.

Questions Asked (1)

Design and implement an incremental task scheduler for a data-labeling platform. Tasks arrive in daily batches and must be assigned to (task, model, human) triples. Each human can only take one task per day, no task gets assigned twice in a day, and a human can never be assigned the same task they labeled before. Assignments should stay balanced across humans and models over time, and the scheduler must support incremental updates using maintained state rather than recomputing from scratch each day.

System DesignAlgorithms & Data StructuresTechnical Trade-offs

Author's notes

This one took me a while to even parse.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then propose a data model and incremental algorithm that maintains state across days. Discuss trade-offs between different approaches (e.g., greedy vs. optimization) and how to handle scale and failures.

Pro tip: Emphasize the importance of maintaining state incrementally and handling edge cases like task exhaustion or human availability changes. Show awareness of real-world constraints like fairness and avoiding past assignments.

1. Clarify Requirements and Constraints

Ask questions to understand scale, latency, consistency needs, and what 'balanced' means. Confirm the rules: one task per human per day, no duplicate task per day, and no repeat of past human-task pairs.

2. Design Data Model and State

Define entities: tasks, humans, models, and assignments. Propose maintaining state such as assignment counts per human/model, past assignments, and daily availability.

3. Propose Incremental Assignment Algorithm

Outline a greedy or matching-based algorithm that processes new tasks daily, respecting constraints and balancing load. Use maintained state to avoid recomputation.

4. Address Scalability and Trade-offs

Discuss how the design scales with increasing tasks/humans, potential bottlenecks, and trade-offs between optimality and efficiency. Mention possible use of priority queues or bipartite matching.

5. Handle Failures and Edge Cases

Explain how to handle scenarios like no available humans, task exhaustion, or state corruption. Propose idempotent updates and recovery mechanisms.

Key Points to Mention

Incremental state maintenance: track assignment counts, past assignments, and daily availability to avoid full recomputation.
Balancing strategy: use round-robin, least-loaded, or weighted fair sharing to distribute tasks across humans and models.
Constraint satisfaction: ensure one task per human per day, no duplicate task per day, and no repeat of past human-task pairs.
Algorithm choice: greedy assignment with priority queues or bipartite matching for optimality, considering time complexity.
Scalability: partition by day or human groups, use efficient data structures (e.g., heaps, hash maps) for lookups.
Trade-offs: discuss fairness vs. throughput, optimality vs. simplicity, and how to handle dynamic changes in task/human pools.

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