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Design a regional surge pricing strategy

Last updated: Mar 29, 2026

Quick Overview

This question evaluates skills in designing real-time surge pricing systems, assessing competencies in machine learning modeling, control and pricing logic, data engineering, operational robustness, explainability, and monitoring within the System Design domain.

  • hard
  • OpenAI
  • System Design
  • Machine Learning Engineer

Design a regional surge pricing strategy

Company: OpenAI

Role: Machine Learning Engineer

Category: System Design

Difficulty: hard

Interview Round: Onsite

## Scenario You operate a ride-hailing platform. You need to design a system that sets **surge multipliers** (dynamic pricing) for a given region. ## Task Design: - A pricing strategy that balances rider experience, driver supply, and marketplace efficiency. - A production system that computes and applies surge in near real time. ## Requirements - Update every 1–5 minutes. - Prevent extreme volatility (surge spikes/flapping). - Be robust to fraud and sudden demand shocks (events, weather). - Provide explainability and monitoring. ## Deliverables - Modeling approach and control logic. - Data inputs and architecture. - Metrics and experimentation plan. - Safety constraints and edge cases.

Quick Answer: This question evaluates skills in designing real-time surge pricing systems, assessing competencies in machine learning modeling, control and pricing logic, data engineering, operational robustness, explainability, and monitoring within the System Design domain.

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OpenAI logo
OpenAI
Dec 15, 2025, 12:00 AM
Machine Learning Engineer
Onsite
System Design
23
0

Scenario

You operate a ride-hailing platform. You need to design a system that sets surge multipliers (dynamic pricing) for a given region.

Task

Design:

  • A pricing strategy that balances rider experience, driver supply, and marketplace efficiency.
  • A production system that computes and applies surge in near real time.

Requirements

  • Update every 1–5 minutes.
  • Prevent extreme volatility (surge spikes/flapping).
  • Be robust to fraud and sudden demand shocks (events, weather).
  • Provide explainability and monitoring.

Deliverables

  • Modeling approach and control logic.
  • Data inputs and architecture.
  • Metrics and experimentation plan.
  • Safety constraints and edge cases.

Solution

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