This was an interesting one because it combines real-time systems with economics.

I started with the data pipeline: real-time supply (available drivers) and demand (ride requests) per geographic hexagon (H3 cells). The pricing model adjusts multipliers based on the supply-demand ratio.

Where I think I fell short was the discussion about market dynamics. The interviewer wanted me to think about how surge pricing affects both supply and demand simultaneously. Higher prices reduce demand but attract more drivers. I didn't model this feedback loop well enough.

Also discussed anti-gaming measures. Drivers could turn off the app and wait for surge, or passengers could move to adjacent zones with lower surge. Talked about smoothing algorithms and minimum surge duration to prevent manipulation.

The follow up about fairness and price discrimination was tough. How do you prevent the system from consistently surging in lower-income neighborhoods? I proposed geographic fairness constraints but the solution felt incomplete.

Got rejected. The feedback mentioned I needed to think more holistically about the economic model, not just the technical implementation.