OpenAI System Design Interview Questions

Treat tokens per second as platform efficiency and p95 latency as user experience. Show how batch size, queue time, and early streaming of first tokens balance throughput and latency within the stated SLO.

Batching boosts GPU utilization, KV cache reuses attention state across steps, and speculative decoding uses a fast draft model that the larger model verifies. The trio cuts compute per token and lowers end-to-first-token latency.

Choose SSE for simple one-way token streams and broad proxy support. Choose WebSockets when you need bidirectional control for tool calls, cancellations, or progress, and define heartbeats and backpressure either way.

Apply admission control at the edge, set token budgets per request, cap queue time, and return 429 with Retry-After. Inside the cluster, use fair queues and drop lowest priority traffic first to protect SLOs.

The OpenAI System Design Interview for L4/L5 (senior/staff) engineers emphasizes leadership in architectural decision-making, handling ambiguous problems, and aligning technical trade-offs with business goals.

Some of the hardest OpenAI System Design Interview questions revolve around scaling LLM inference, GPU resource management, fraud detection, and multi-region system resilience.

The OpenAI System Design Interview may include questions like designing GitHub Actions to test how you approach workflow orchestration, developer productivity, and scalable CI/CD systems.

In the OpenAI System Design Interview, “design ChatGPT” challenges candidates to model a conversational AI platform with low latency, high throughput, and reliability across millions of users.

This OpenAI System Design Interview question tests your ability to design efficient serving pipelines that manage GPU resources, model versions, and request scheduling.

In the OpenAI System Design Interview, this problem examines your knowledge of similarity search, indexing strategies, and scaling storage for embeddings.

Candidates in the OpenAI System Design Interview are expected to discuss replication, traffic routing, and disaster recovery when handling multi-region inference.

An OpenAI System Design Interview may ask you to design autoscaling for GPU clusters, where you must balance cost efficiency with real-time workload spikes.

The OpenAI System Design Interview includes fine-tuning scenarios to test how you handle distributed job scheduling, fairness, and fault tolerance.

Caching is a frequent OpenAI System Design Interview topic, requiring you to discuss TTL strategies, eviction policies, and reducing model inference costs.

In the OpenAI System Design Interview, fraud detection design questions test your ability to detect anomalies, rate limit suspicious users, and prevent misuse.

The OpenAI System Design Interview for backend engineers emphasizes database design, service orchestration, and API performance.

For research engineers, the OpenAI System Design Interview focuses more on ML workflows, model deployment, and experimentation platforms.

The OpenAI System Design Interview is often considered more AI- and GPU-focused, while FAANG system design interviews cover broader distributed systems scenarios.

This OpenAI System Design Interview scenario evaluates how you handle hardware-software integration, monitoring, and optimizing inference on custom chips.

Distributed systems concepts like CAP theorem, consensus, replication, and sharding are common in the OpenAI System Design Interview.

The OpenAI System Design Interview often has two design screens to assess both high-level architecture and deeper implementation trade-offs.

Top tips for the OpenAI System Design Interview include practicing AI-specific scenarios, clarifying requirements, discussing trade-offs, and sketching system diagrams.

The OpenAI System Design Interview may require designing observability systems to track request latency, token usage, and anomaly detection at scale.