ML
ML Interview Roadmap
← Study plan·System Design

Day 111 of 133

Design Uber ETA + dynamic pricing + DSA review

DeepETA features; surge pricing optimization.

DSA · NeetCode Greedy

  • Jump GameDSA · Greedy

    Interview questions to prep

    1. Why does greedy (track max-reach) work? Where would DP be unnecessary?
    2. How does jump-game-ii (min jumps) differ in approach?

ML System Design · ETA & pricing

  • Design Uber/DoorDash ETA predictionML System DesignUber

    Interview questions to prep

    1. Walk me through Uber's DeepETA — what features and architecture?
    2. How do you handle live traffic, weather, and rare events?
  • Design surge pricing / dynamic pricingML System DesignUber

    Interview questions to prep

    1. How would you design surge pricing — what's the optimization objective and constraints?
    2. How do you avoid the price-spiral feedback loop where surge causes drivers to game positions?
    3. How would you design an Airbnb-style home-value or nightly-price prediction model and separate explainability from optimization?
    4. What features, leakage risks, and geographic cold-start issues matter for home-value prediction?

References & further reading