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Prioritize rooms for allocation

Last updated: Mar 29, 2026

Quick Overview

This question evaluates a candidate's ability to design a real-time room allocation and ranking service in the System Design domain, testing competencies in architectural design, ranking and scoring functions, data sourcing and freshness, fairness mechanisms, and online evaluation and experimentation.

  • hard
  • Uber
  • System Design
  • Software Engineer

Prioritize rooms for allocation

Company: Uber

Role: Software Engineer

Category: System Design

Difficulty: hard

Interview Round: Onsite

Design a room allocation service that, given a meeting request (time window, duration, capacity, equipment, location), returns a ranked list of rooms. Propose a scoring function integrating factors such as historical room usage counts, expected idle-time fragmentation, attendee walking distance, equipment match, past no-show rates, and fairness across teams. Describe data sources and freshness, how weights are tuned (rules vs learned model), safeguards against starvation, online/offline evaluation, and an A/B testing plan.

Quick Answer: This question evaluates a candidate's ability to design a real-time room allocation and ranking service in the System Design domain, testing competencies in architectural design, ranking and scoring functions, data sourcing and freshness, fairness mechanisms, and online evaluation and experimentation.

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Uber logo
Uber
Sep 6, 2025, 12:00 AM
Software Engineer
Onsite
System Design
14
0

System Design: Room Allocation and Ranking Service

Context and assumptions:

  • The client sends a meeting request with: (a) time window or fixed start/end, (b) duration, (c) attendee count, (d) required equipment, and (e) preferred location/building.
  • The service returns a top-N ranked list of candidate rooms (or room-time pairs when a window is provided) that can be immediately reserved.
  • Hard constraints (capacity, equipment, availability) must be met; remaining factors are optimized via a score.

Requirements

  1. API returns a ranked list of rooms (or room-time suggestions within the window) that satisfy hard constraints.
  2. Propose a scoring function that integrates:
    • Historical room usage counts
    • Expected idle-time fragmentation
    • Attendee walking distance
    • Equipment match quality
    • Past no-show rates
    • Fairness across teams
  3. Describe:
    • Data sources and freshness (online vs offline features)
    • Weight tuning strategy (rule-based vs learned model)
    • Safeguards against starvation (rooms/teams)
    • Online/offline evaluation
    • A/B testing plan

Solution

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