Openai·Software Engineer·Onsite - System Design / Architecture·Staff

StaffPrefer not to say

May 2026

Summary

System design round at OpenAI, one big open-ended question about building a distributed task scheduler from scratch. Felt like they wanted to see how far you could go before running out of things to say, and I definitely found that ceiling.

Questions Asked (1)

Design a distributed task scheduler similar to Quartz or Airflow that can schedule and execute background jobs across a fleet of machines. Cover the API for submitting, canceling, and querying jobs, scheduling semantics like cron, fixed-delay, fixed-rate, one-shot, and DAG dependencies, a persistent task store, leader election to prevent double-execution, worker pool management, failure recovery, exactly-once vs at-least-once execution guarantees, retries with backoff, timeouts, fairness across tenants, and observability.

System DesignTechnical Trade-offsAPI & Integrations

Author's notes

This question is basically a whole system in one prompt.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale, then present a high-level architecture with core components: API, scheduler, task store, leader election, and workers. Dive into scheduling semantics, execution guarantees, and failure handling, explicitly discussing trade-offs (e.g., exactly-once vs at-least-once) and how they influence design choices.

Pro tip: Emphasize idempotency and deduplication as key to achieving exactly-once semantics, and discuss how to handle clock skew and distributed coordination (e.g., using leases) to prevent double-execution.

1. Clarify Requirements and Scope

Ask about scale (jobs per second, number of workers), latency requirements, and consistency needs. Confirm whether exactly-once is required or if at-least-once with idempotency is acceptable.

2. High-Level Architecture

Outline components: API gateway for job submission/management, scheduler service for triggering, persistent task store (e.g., SQL or NoSQL), leader election (e.g., using ZooKeeper/etcd), and worker pool. Explain how they interact.

3. Scheduling Semantics and API

Define API endpoints for submit, cancel, query. Describe support for cron, fixed-delay, fixed-rate, one-shot, and DAG dependencies. Explain how schedules are stored and evaluated.

4. Execution Guarantees and Failure Handling

Discuss leader election to avoid double-triggering, task leasing, retries with exponential backoff, timeouts, and dead-letter queues. Compare exactly-once vs at-least-once and how to achieve each.

5. Scalability, Fairness, and Observability

Explain worker pool management, autoscaling, fairness across tenants (e.g., weighted queues), and observability (metrics, logging, tracing). Mention trade-offs like consistency vs availability.

Key Points to Mention

Leader election using distributed consensus (e.g., Raft, ZooKeeper) to ensure only one scheduler instance triggers tasks.
Persistent task store with ACID transactions or strong consistency for job state and scheduling metadata.
Exactly-once execution via idempotent tasks and deduplication (e.g., unique job execution IDs), or at-least-once with retries and idempotency.
Retry policies with exponential backoff and jitter, plus timeout handling and dead-letter queues for failed tasks.
Fairness across tenants using weighted fair queuing or resource quotas to prevent noisy neighbors.
Observability: metrics (job success/failure rates, latency), distributed tracing, and logging for debugging and monitoring.

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