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

Senior
May 2026

Summary

OpenAI software engineer onsite, system design round focused on building a payment abstraction layer sitting between users and upstream processors like Amex. Pretty standard fintech design problem but the scope was broader than I expected.

Questions Asked (1)

Q1

Design a payment system that sits as an intermediary layer between users and upstream payment processors (e.g. Amex).

System DesignAPI & IntegrationsTechnical Trade-offs
Author's notes

The core challenge was scoping it right.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints (e.g., scale, consistency, supported payment methods) before diving into the design. Then propose a high-level architecture that abstracts upstream processors, ensuring idempotency, reliability, and security. Finally, discuss trade-offs and how you would handle failures and scaling.

Pro tip: Emphasize idempotency and exactly-once processing as critical for payment systems, and mention how you would handle partial failures and reconciliation with upstream processors.

1. Clarify Requirements

Ask questions to understand scale, latency, consistency needs, supported payment methods, and compliance requirements. This ensures the design meets the actual needs.

2. High-Level Architecture

Outline the main components: API gateway, payment service, processor adapters, database, and message queue. Explain how they interact to process a payment.

3. Deep Dive into Key Flows

Detail the payment authorization, capture, and refund flows, including idempotency, retries, and error handling. Discuss how to ensure data consistency across services.

4. Address Trade-offs and Scaling

Discuss trade-offs between consistency and availability, choice of database, and how to scale horizontally. Mention monitoring, alerting, and reconciliation with upstream processors.

Key Points to Mention

  • Idempotency keys to prevent duplicate charges
  • Asynchronous processing with message queues for reliability
  • Adapter pattern to abstract different upstream processors
  • Data consistency and transaction management (e.g., saga pattern)
  • Security and compliance (PCI DSS, tokenization)
  • Monitoring, logging, and reconciliation with upstream processors

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