Uber Interview Questions 2026

Design Uber's driver-rider matching system. How do you match the nearest available driver to a rider within 200ms at global scale? Cover: geospatial indexing (H3/S2), the dispatch algorithm, how you handle supply-demand imbalances, and what happens when a matched driver rejects the trip.

Design Uber's dynamic pricing (surge) system. When supply is low and demand is high in a geofenced zone, prices increase automatically. Explain the data pipeline, the pricing model, how you prevent oscillations (price going up → drivers rush in → price drops → drivers leave), and regulatory constraints.

Design Uber Eats' order batching system. A single delivery partner picks up orders from multiple nearby restaurants and delivers to multiple customers in one trip. How do you optimise the route and assignment?

Design Uber's payment reconciliation system. Uber collects payments in 70+ countries with different currencies, payment methods, and regulations. How do you reconcile driver payouts against rider charges and detect discrepancies?

Describe a time you had to work with a very ambiguous technical requirement. How did you clarify scope, make progress, and manage stakeholder expectations during the process?

Describe a time you had to make a decision with incomplete data under time pressure. What was your reasoning process?

Given a city map as a weighted graph, find the shortest travel time from a driver's current location to all potential passenger pickup points simultaneously. How does Dijkstra's multi-source variant solve this?

Uber uses H3 hexagonal geospatial indexing. Explain why hexagons are better than squares or lat/lng grids for proximity searches. How do you find all drivers within 2km of a rider using H3 resolution levels?

Given a list of GPS coordinates representing a driver's path, compute the total distance travelled. Then find the longest contiguous segment where the driver was stationary (speed < 5 km/h). Discuss floating-point precision issues.

Your pipeline SLA requires data to be available in the warehouse by 6 AM. Yesterday it missed at 7:30 AM. Walk me through your incident response.

Your PySpark job has data skew on a join key (user_id) where 1% of users account for 60% of the data. How do you handle it?

Design Uber's surge pricing system. It must calculate real-time multipliers per area, update every minute, handle 1 million location events per second from drivers, and serve the price to 10 million concurrent users.

Design a ride-sharing system like Uber. Focus on matching riders to drivers in real-time.

Design the rider-driver matching system for a ride-hailing app. How would you balance minimizing rider wait time against fairness in driver earnings distribution?

You need to find all available drivers within a 3km radius of a rider efficiently, with millions of driver location updates per second. What indexing structure would you use and why not a naive distance calculation over all drivers?

Trip fares are occasionally being calculated twice, double-charging riders. The fare calculation service is stateless and horizontally scaled behind a queue. Where would you look first, and what's the likely root cause?

You need to track the top 10 most active drivers by trip count, updated in real time. Which Redis data structure fits this, and why not just a plain hash with manual sorting?

Design a rate limiter for a public API that must work correctly across multiple application server instances, not just per-instance.