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

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Apr 2026

Summary

System design round at OpenAI for a backend/infrastructure role. The question was a full payment platform design, which sounds scoped until you realize they want fraud, reconciliation, idempotency, AND compliance all in one go. Left feeling like I covered maybe 60% of what they were looking for.

Questions Asked (1)

Design a payment processing system that supports cards, bank transfers, and digital wallets, with idempotent transactions, retry logic, refund flows, fraud detection, and reconciliation against external payment processors. Address consistency, scalability, failure handling, and audit/compliance requirements.

System DesignTechnical Trade-offsAPI & Integrations

Author's notes

I spent the first chunk of time on the data model and API layer, which felt safe but probably wasn't what they cared most about.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale, then design a high-level architecture with idempotency, retries, and reconciliation as core components. Walk through the transaction lifecycle, failure handling, and trade-offs, emphasizing consistency, scalability, and compliance.

Pro tip: Emphasize idempotency and reconciliation as the backbone of reliability, and discuss how you'd handle partial failures and exactly-once semantics in a distributed system.

1. Clarify Requirements and Scale

Ask about expected throughput, latency, supported payment methods, regulatory requirements, and consistency needs. Define functional and non-functional requirements.

2. High-Level Architecture

Outline core services: API gateway, payment orchestrator, processor adapters, idempotency store, retry queue, fraud detection, reconciliation, and audit log. Choose appropriate data stores and messaging.

3. Transaction Lifecycle and Idempotency

Detail how a payment flows from request to completion, including idempotency key handling, state machine, and exactly-once processing. Discuss retries with exponential backoff and dead-letter queues.

4. Failure Handling and Consistency

Explain how to handle partial failures, timeouts, and processor errors. Discuss consistency models (e.g., eventual consistency for reconciliation) and compensating transactions for refunds.

5. Scalability, Compliance, and Trade-offs

Address scaling strategies (sharding, partitioning), audit/compliance (PCI DSS, GDPR), and trade-offs between consistency, availability, and latency (CAP theorem).

Key Points to Mention

Idempotency keys and deduplication to ensure exactly-once processing
Retry logic with exponential backoff, jitter, and dead-letter queues
Fraud detection integration (e.g., rule-based, ML models) and real-time scoring
Reconciliation processes with external processors, including batch and real-time
Audit trails and compliance (PCI DSS, GDPR, SOX) with immutable logs
Scalability via horizontal scaling, sharding, and asynchronous processing

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