OpenAI interview questions and how to prepare

• First-principles problems with deliberate ambiguity; reasoning depth wins.
• Expect follow-ups on second-order effects and safety implications.

1. Imagine our latest large language model (LLM) is experiencing intermittent latency spikes during peak usage. Describe how you would go about debugging this issue, starting from initial hypotheses to potential solutions. What metrics would you track, and how would you differentiate between network, compute, or model-specific bottlenecks?

   Software EngineerDebugging & PerformanceTests: Evaluates debugging methodology, understanding of distributed systems at scale, and ability to diagnose complex performance issues.

2. Design a system to efficiently serve real-time embeddings for millions of users interacting with a product like ChatGPT. Consider aspects like data storage, retrieval speed, scaling, and cost. How would you handle updates to the embedding models?

   Software EngineerSystem DesignTests: Assesses system design skills, understanding of distributed databases, caching strategies, and scalability for high-throughput, low-latency services.

3. We are developing a new feature that allows users to fine-tune our LLMs on their private datasets. Outline the technical challenges and potential security vulnerabilities you foresee. How would you design the system to ensure data privacy and model integrity?

   Software EngineerSecurity & System DesignTests: Tests understanding of security best practices, data privacy concerns in ML systems, and system design for sensitive data handling.

4. Given a scenario where a new LLM release shows a statistically significant increase in helpfulness scores but a slight decrease in perceived safety scores in user feedback, how would you approach this trade-off? What further analysis would you perform before recommending a rollout?

   Software EngineerReasoning & Trade-offsTests: Evaluates ability to reason about complex trade-offs, apply first principles thinking to nuanced problems, and understand the interplay between performance and safety.

5. You've deployed a new reinforcement learning from human feedback (RLHF) component that seems to be improving conversational coherence. However, some users report the model is becoming too verbose. How would you quantify 'verboseness' and design an experiment to test if the RLHF component is the cause and to what extent?

   Software EngineerExperimentation & DebuggingTests: Assesses ability to define and measure abstract concepts like 'verboseness' and design experiments to isolate the impact of a specific system component.

6. Describe a situation where you had to break down a complex technical problem into smaller, manageable parts. Apply this approach to a hypothetical scenario: optimizing the inference speed of a large transformer model for a consumer-facing API.

   Software EngineerProblem DecompositionTests: Evaluates analytical skills and the ability to deconstruct complex problems into actionable steps.

72+ more OpenAI-style questions are in the free library, each practiceable live with adaptive follow-ups and an honest scorecard. Start free.