The open-ended framing is deceptively hard.
Choose a system you know deeply, ideally one with ML components, and structure your answer around the problem, architecture, data model, scaling, and trade-offs. Be specific about your role and decisions, and quantify impact where possible. Tailor to OpenAI by emphasizing ML-specific challenges like data pipelines, model serving, and experimentation.
Pro tip: Focus on trade-offs and failures, not just successes—showing how you navigated constraints and learned from mistakes demonstrates senior-level maturity. Also, connect your choices to business or user impact to show product sense.
Briefly describe the system's purpose, users, and key functional and non-functional requirements (e.g., latency, scale, accuracy). Clarify your specific role and the team size.
Sketch the main components (e.g., data ingestion, training pipeline, model serving, monitoring) and how they interact. Mention technologies used and why.
Describe how data is structured, stored, and processed. Cover schema design, feature engineering, data versioning, and any ML-specific considerations like label generation.
Explain how the system handles growth in data, traffic, or model complexity. Cover horizontal scaling, distributed training, caching, and bottleneck mitigation.
Articulate the major decisions you made (e.g., batch vs. real-time, model complexity vs. latency) and the trade-offs involved. Share what you would do differently and why.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.