The format is slides, and the interviewer will dig into whatever you put up there.
Choose a project where you owned significant technical decisions, and narrate it as a story: context, problem, options considered, decision rationale, implementation, and measurable outcome. Focus on the 'why' behind each choice, including trade-offs and what you'd do differently, to show engineering judgment rather than just listing technologies.
Pro tip: Anchor every technical decision to a concrete constraint (latency, cost, scale, team size) and quantify the impact; at OpenAI, interviewers care more about how you reason under ambiguity than about which framework you used.
Briefly describe the project's goal, your specific role, the team size, and the key constraints (timeline, scale, reliability, budget) that shaped your decisions. Keep this to 30-60 seconds so you leave room for the technical depth.
State the central engineering challenge in one or two sentences, e.g., 'We needed to serve 10k QPS with p99 under 200ms while keeping inference costs flat.' This gives the interviewer a clear lens for evaluating your decisions.
For each major decision, explain the options you considered, the criteria you used, and why you chose one over the others. Explicitly name the trade-offs you accepted (e.g., consistency vs. availability, build vs. buy).
Summarize how you executed, how you de-risked the approach (prototypes, load tests, canary rollouts), and how you measured success. Mention any course corrections you made based on data.
Share quantified results (latency, cost, revenue, adoption) and one or two things you'd do differently. This demonstrates self-awareness and growth, which senior engineers value highly.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Acknowledge the alternative approach as valid, then clearly state the specific requirements and constraints that made your chosen approach more suitable. Compare the trade-offs (e.g., performance, scalability, complexity, cost) and explain how your choice aligned with the system's goals and priorities.
Pro tip: Show that you evaluated multiple options and that your decision was data-driven, not based on personal preference. Mention any metrics or benchmarks you used to validate your choice.
Briefly describe the system, its goals, and the key constraints (e.g., latency, throughput, consistency, budget) that influenced the design.
Show respect for the alternative approach and explain why it might be a good fit in other scenarios.
Analyze the pros and cons of both approaches in the context of the requirements, focusing on factors like performance, scalability, complexity, and maintainability.
Clearly state why your chosen approach was better aligned with the requirements, and mention any data or experiments that supported the decision.
If possible, share the results (e.g., improved metrics) and what you learned, showing openness to revisit the decision if conditions change.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the current system architecture and load characteristics, then systematically analyze each component's scalability limits and failure modes under 10x load. Propose a combination of vertical scaling, horizontal scaling, caching, and architectural changes, while discussing trade-offs and validation through load testing.
Pro tip: Quantify the impact: estimate the new resource requirements (e.g., 10x QPS, storage, bandwidth) and identify the first bottleneck. This shows you think in terms of concrete numbers and prioritization, which is highly valued at OpenAI.
Ask about the current load metrics (QPS, data volume, latency SLAs) and system architecture. Confirm whether the 10x increase is uniform or affects specific components disproportionately.
Analyze each layer (load balancers, app servers, databases, caches, queues) to find components that cannot handle 10x load. Consider both capacity limits and failure modes.
Outline horizontal scaling (adding instances), vertical scaling (upgrading hardware), and architectural changes (sharding, caching, async processing). Prioritize based on cost and complexity.
Compare options in terms of cost, complexity, and impact on latency/consistency. Suggest a phased approach with monitoring and rollback plans.
Describe how to test the system under 10x load using tools like JMeter or Locust, and what metrics to monitor (latency, error rates, resource utilization) to ensure success.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.