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

Senior

Jun 2026

Summary

OpenAI system design round for a software engineering role, and it was basically a full ML systems deep-dive crammed into one question. The scope was enormous and I kept second-guessing whether to go broad or deep.

Questions Asked (1)

Design a complete machine learning system that ranks multiple candidate text responses to a user query, with the goal of maximizing user satisfaction. Cover problem formulation, data and labeling strategy, model choice, training pipeline, evaluation metrics, online inference architecture, experimentation, safety measures, bias and privacy controls, monitoring, retraining cadence, and cost trade-offs.

System DesignA/B Testing & ExperimentationTechnical Trade-offs

Author's notes

This was one question that took the entire session.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem scope and constraints, then walk through the ML system design in a structured manner covering data, modeling, evaluation, deployment, and iteration. Emphasize trade-offs and how you would measure success, while highlighting safety, bias, and privacy considerations.

Pro tip: Anchor your design around a clear objective function tied to user satisfaction, and propose a robust offline evaluation that correlates with online metrics to de-risk deployment.

1. Problem Formulation and Data Strategy

Define the ranking task, success metrics, and data needs. Outline labeling strategies and data collection pipelines.

2. Model Selection and Training Pipeline

Choose a model architecture suitable for ranking (e.g., pairwise or listwise), and describe the training pipeline including feature engineering and loss functions.

3. Evaluation and Experimentation

Define offline and online evaluation metrics, and design A/B tests to measure impact on user satisfaction.

4. Deployment and Inference Architecture

Design a scalable, low-latency inference system with monitoring, safety, and privacy controls.

5. Iteration and Maintenance

Plan for retraining cadence, bias mitigation, cost optimization, and continuous monitoring.

Key Points to Mention

Use of pairwise or listwise ranking losses (e.g., RankNet, LambdaMART) and how they align with user satisfaction.
Importance of human labeling and implicit feedback (e.g., clicks, dwell time) for training data.
Offline metrics like NDCG and online metrics like CTR, satisfaction scores, and A/B testing methodology.
Safety measures: toxicity filtering, bias detection, and privacy-preserving techniques (e.g., differential privacy).
Inference architecture: model serving, caching, and latency considerations.
Cost trade-offs: model size vs. latency, retraining frequency, and infrastructure costs.

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