Uber
Ride-hailing and delivery platform with massive real-time data challenges.
4 Rounds
~21 Days
Hard
The Interview Loop
Recruiter Screen (30 min)
Standard fit check, behavioral questions, and resume overview.
Technical Loop (3-4 Rounds)
Deep dive into domain knowledge, coding, and system design.
Interview Question Bank
Cloud Engineer
•
Behavioral
•
medium
Describe a situation where a critical production system went down under your watch. How did you handle the incident and what was the post-mortem process?
#Incident Response
#Accountability
#Continuous Improvement
Cloud Engineer
•
Behavioral
•
medium
Uber's culture emphasizes 'Build with Heart'. Give an example of how you incorporated user empathy or developer experience into an infrastructure or tooling project.
#Developer Experience
#Empathy
#Productivity
Cloud Engineer
•
Behavioral
•
medium
Tell me about a time you had to push back on a software engineering team because their proposed architecture was not scalable or secure enough for a production cloud environment.
#Communication
#Conflict Resolution
#Security
#Collaboration
Cloud Engineer
•
Coding
•
medium
Given a list of server objects with their current CPU load and memory usage, write a load balancer function that distributes incoming requests to the least loaded server.
#Data Structures
#Load Balancing
#Optimization
Cloud Engineer
•
Coding
•
medium
Write a Python or Go script to parse a large access log file, identify the top 10 IP addresses with the highest 5xx error rates, and automatically block them using a cloud WAF API.
#Python
#Go
#Log Parsing
#API Integration
Cloud Engineer
•
Coding
•
hard
Implement a rate limiter using a token bucket algorithm to prevent API abuse from a single client ID.
#Concurrency
#Rate Limiting
#System Design Implementation
Cloud Engineer
•
System Design
•
hard
Design a multi-region, active-active deployment architecture for Uber's ride-matching service to ensure zero downtime during a regional cloud outage.
#High Availability
#Multi-region
#Disaster Recovery
#Cloud Architecture
Cloud Engineer
•
System Design
•
hard
Design a disaster recovery strategy for Uber's payment processing gateway, ensuring an RPO (Recovery Point Objective) of near zero and an RTO (Recovery Time Objective) of under 5 minutes.
#Disaster Recovery
#Databases
#Networking
#Payments
Cloud Engineer
•
System Design
•
hard
Design an observability and metrics pipeline capable of ingesting millions of data points per second from Uber's global fleet of microservices.
#Observability
#Distributed Systems
#Kafka
#Data Pipelines
Cloud Engineer
•
System Design
•
hard
Design an Infrastructure as Code (IaC) provisioning system using Terraform that allows hundreds of Uber engineering teams to deploy resources safely without state conflicts.
#Terraform
#CI/CD
#Infrastructure as Code
#Security
Cloud Engineer
•
Technical
•
hard
Explain the step-by-step process of migrating a legacy monolithic application to a containerized microservices architecture on Kubernetes with zero downtime.
#Migration
#Kubernetes
#Microservices
#Traffic Management
Cloud Engineer
•
Technical
•
medium
Walk me through the lifecycle of a Kubernetes Pod. What exactly happens from the moment 'kubectl apply' is executed to when the container is running?
#Kubernetes
#API Server
#Kubelet
#Container Runtime
Cloud Engineer
•
Technical
•
easy
Explain the differences between an Application Load Balancer (ALB) and a Network Load Balancer (NLB). When would you use each in Uber's infrastructure?
#Load Balancing
#OSI Model
#AWS/GCP
Cloud Engineer
•
Technical
•
medium
Walk me through how you would troubleshoot a sudden spike in 502 Bad Gateway errors across a specific Kubernetes cluster handling UberEats orders.
#Kubernetes
#Troubleshooting
#Networking
#Observability
Cloud Engineer
•
Technical
•
medium
How do you manage secrets, API keys, and sensitive configuration in a multi-cloud environment?
#Secret Management
#HashiCorp Vault
#IAM
#Compliance
Data Engineer
•
Behavioral
•
medium
Describe a time when you had to push back on a stakeholder or Product Manager regarding a data engineering requirement, such as an unrealistic deadline or a fundamentally flawed metric definition.
#Communication
#Stakeholder Management
#Prioritization
Data Engineer
•
Behavioral
•
medium
Tell me about a time you discovered a critical data quality issue in a production pipeline. How did you troubleshoot it, and what safeguards did you put in place to prevent recurrence?
#Data Quality
#Incident Management
#Ownership
Data Engineer
•
Coding
•
medium
Given a list of driver online sessions as [start_time, end_time] pairs, write a Python function to merge all overlapping sessions and return the total time the driver was online and available for dispatch.
#Arrays
#Sorting
#Intervals
Data Engineer
•
Coding
•
hard
Given a continuous stream of trip distances, design a data structure and write the Python code to return the median trip distance at any given time efficiently.
#Heaps
#Data Streams
#Design
Data Engineer
•
Coding
•
medium
Write a SQL query to calculate the rolling 7-day average of canceled rides per rider. The output should include the rider_id, date, and the 7-day moving average.
#Window Functions
#Moving Averages
#Time Series
Data Engineer
•
Coding
•
medium
Write a SQL query to find the top 3 drivers by total earnings in each city over the last 30 days. Only include completed trips and account for potential ties in earnings.
#Window Functions
#Aggregations
#Date/Time Functions
Data Engineer
•
Coding
•
medium
Write a SQL query to find riders who have taken both an UberX ride and placed an Uber Eats order within the exact same 24-hour window.
#Joins
#Date/Time Math
#Correlated Subqueries
Data Engineer
•
Coding
•
easy
Write a Python script to parse a large, nested JSON log file containing raw GPS pings from driver apps, flatten the structure, and filter out pings with a horizontal accuracy worse than 50 meters.
#Python
#JSON Parsing
#Data Cleaning
Data Engineer
•
System Design
•
hard
Design a real-time data pipeline to calculate surge pricing multipliers. The system needs to aggregate ride requests and available drivers per H3 hexagon (resolution 9) every 30 seconds.
#Stream Processing
#Kafka
#Flink
#Geospatial Data
#State Management
Data Engineer
•
System Design
•
hard
Design the data architecture and ETL pipelines to generate the daily payout reports for Uber Eats restaurants. Consider that restaurants can have different timezone cutoffs and complex, tiered commission structures.
#ETL/ELT
#Batch Processing
#Airflow
#Data Warehousing
Data Engineer
•
System Design
•
hard
Design a system to ingest and process telemetry data (GPS, speed, heading) from millions of active drivers in real-time to power the live map view and feed ETA machine learning models.
#High Throughput Ingestion
#Kafka
#Data Partitioning
#NoSQL
Data Engineer
•
Technical
•
hard
Explain how you would achieve exactly-once processing semantics in a financial data pipeline moving Uber driver payout events from Kafka to a data warehouse using Spark Streaming or Flink.
#Kafka
#Exactly-Once Semantics
#Idempotency
#Two-Phase Commit
Data Engineer
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Technical
•
medium
Design a dimensional data model (Star Schema) to track Uber Eats order lifecycle events (created, accepted by restaurant, picked up, delivered, canceled) to support historical bottleneck and delivery time analysis.
#Dimensional Modeling
#Fact Tables
#Slowly Changing Dimensions
#Event Logging
Data Engineer
•
Technical
•
hard
How would you resolve a severe data skew issue in an Apache Spark job where joining a massive 'trips' table with a 'cities' table causes OutOfMemory errors specifically for the partition handling New York City data?
#Apache Spark
#Performance Tuning
#Data Skew
#Distributed Computing
Data Engineer
•
Technical
•
medium
How do you ensure your Airflow DAGs are idempotent? Why is this specifically critical when backfilling historical trip data after a logic change in the fare calculation model?
#Apache Airflow
#Idempotency
#Backfilling
Data Scientist
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Behavioral
•
medium
Tell me about a time you discovered a significant flaw in your data or model after it was already shared with stakeholders. How did you handle it?
#Communication
#Integrity
#Stakeholder Management
Data Scientist
•
Behavioral
•
easy
Describe a time when you had to explain a complex statistical concept (like p-values or causal inference) to a non-technical product manager.
#Cross-functional Collaboration
#Data Storytelling
Data Scientist
•
Coding
•
medium
Write a SQL query to find the 7-day rolling average of driver cancellation rates for each city. Assume you have a trips table with trip_id, city_id, driver_id, status (completed, cancelled_by_driver, cancelled_by_rider), and request_timestamp.
#Window Functions
#Rolling Averages
#Aggregations
Data Scientist
•
Coding
•
medium
You are given a dataset of Uber rides. Write a Python script to simulate a simple Markov Chain to predict the probability of a driver ending up in City B given they started in City A.
#Probability
#Markov Chains
#Python
#Data Manipulation
Data Scientist
•
Coding
•
medium
Write a SQL query to identify riders who have taken a trip in at least 3 different cities within a 30-day window.
#Self Joins
#Date Functions
#COUNT DISTINCT
Data Scientist
•
Coding
•
medium
Given a list of driver locations (x,y coordinates) and a rider's location, write a Python function to find the k-nearest available drivers. Optimize for performance assuming millions of drivers.
#Heaps
#Spatial Data Structures
#Python
Data Scientist
•
System Design
•
medium
How would you build a model to detect fraudulent rider accounts that are creating fake trips to cash out driver referrals?
#Fraud Detection
#Anomaly Detection
#Classification
Data Scientist
•
System Design
•
hard
How would you design a matching algorithm for UberX Share (formerly UberPool)? What trade-offs do you need to consider between match rate, detour time, and rider experience?
#Graph Algorithms
#Optimization
#Heuristics
#Marketplace Routing
Data Scientist
•
System Design
•
hard
Design the surge pricing algorithm. What are the key inputs, what is the objective function, and how do you balance rider conversion with driver supply?
#Dynamic Pricing
#Marketplace Equilibrium
#Optimization
Data Scientist
•
System Design
•
hard
Design a machine learning system to predict the Estimated Time of Arrival (ETA) for a trip. What features would you use, and how would you evaluate the model?
#ETA Prediction
#Feature Engineering
#Regression
#Model Evaluation
Data Scientist
•
Technical
•
hard
Uber wants to test a new driver matching algorithm. However, drivers and riders share the same marketplace, meaning a standard A/B test might suffer from interference. How would you design this experiment?
#A/B Testing
#Switchback Testing
#Network Effects
#Marketplace Interference
Data Scientist
•
Technical
•
hard
We rolled out a new driver incentive program in Chicago last month without an A/B test. How would you estimate the causal impact of this program on driver supply?
#Difference-in-Differences
#Synthetic Control
#Observational Data
Data Scientist
•
Technical
•
medium
Uber Eats is considering launching a new 'Healthy Options' carousel on the home screen. What metrics would you look at to determine if this feature is successful?
#Product Metrics
#Uber Eats
#Cannibalization
Data Scientist
•
Technical
•
medium
Suppose the number of completed UberX trips drops by 10% week-over-week, but the number of active app opens remains the same. Walk me through how you would investigate the root cause.
#Root Cause Analysis
#Funnel Analysis
#Marketplace Dynamics
Data Scientist
•
Technical
•
hard
We want to introduce a subscription pass (Uber One) that gives free delivery and discounted rides. How would you determine the optimal monthly price for this pass?
#Pricing Strategy
#LTV/CAC
#Elasticity
#Subscription Models
Machine Learning Engineer
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Behavioral
•
medium
Describe a situation where you believed a complex ML model was necessary, but the engineering or product team wanted a simple heuristic. How did you handle the disagreement?
#Communication
#Trade-offs
#Collaboration
Machine Learning Engineer
•
Behavioral
•
medium
Tell me about a time a machine learning model you deployed degraded in production or caused a business metric to drop. How did you detect it, and what steps did you take to resolve it?
#Incident Response
#Ownership
#Monitoring
Machine Learning Engineer
•
Coding
•
medium
Implement a rate limiter for the Uber API to prevent abuse. The rate limiter should allow a maximum of N requests per user per minute.
#Data Structures
#Concurrency
#Sliding Window
Machine Learning Engineer
•
Coding
•
hard
Given a city map represented as a weighted directed graph where nodes are intersections and edges are roads with travel times, write a function to find the top K shortest paths between a rider and a destination.
#Graphs
#Dijkstra
#A* Search
#Priority Queue
Machine Learning Engineer
•
Coding
•
medium
Implement an autocomplete system for the Uber app destination search. Given a prefix, return the top 5 most frequently searched locations.
#Trie
#Hash Map
#String Manipulation
#Design
Machine Learning Engineer
•
Coding
•
medium
Given an array of ride requests with start and end times, find the minimum number of drivers required to fulfill all requests.
#Arrays
#Sorting
#Greedy
#Heap
Machine Learning Engineer
•
System Design
•
hard
Design Uber's surge pricing model. How would you predict demand and supply in a given geohash for the next 10 minutes?
#Time Series
#Geospatial Data
#Regression
#Real-time ML
Machine Learning Engineer
•
System Design
•
hard
Design a restaurant recommendation system for Uber Eats. How do you handle the cold start problem for new users and new restaurants?
#Recommendation Systems
#Collaborative Filtering
#Two-Tower Models
#Cold Start
Machine Learning Engineer
•
System Design
•
medium
Design a real-time fraud detection system for Uber accounts (e.g., detecting stolen credit cards or fake driver accounts). How do you deal with highly imbalanced data?
#Anomaly Detection
#Classification
#Imbalanced Data
#Graph Neural Networks
Machine Learning Engineer
•
System Design
•
hard
Design the ML architecture for dispatching drivers to riders. How do you balance minimizing rider wait time with maximizing driver earnings and system throughput?
#Reinforcement Learning
#Bipartite Matching
#Operations Research
#Multi-objective Optimization
Machine Learning Engineer
•
Technical
•
medium
How would you generate embeddings for Uber Eats restaurants to capture semantic similarities (e.g., a user who likes 'Spicy Thai' might like 'Sichuan Chinese')? Describe the data and architecture.
#Embeddings
#NLP
#Word2Vec
#GraphSAGE
Machine Learning Engineer
•
Technical
•
medium
For predicting the exact location (latitude/longitude) of a rider's pickup spot based on historical pin drops, what loss function would you use and why? How would you handle outliers?
#Loss Functions
#Geospatial
#Regression
#Robust Statistics
Machine Learning Engineer
•
Technical
•
easy
Uber heavily uses XGBoost for tabular data but Deep Learning for ETA and NLP. Explain the theoretical differences between Gradient Boosted Trees and Neural Networks. When would you strictly prefer one over the other?
#XGBoost
#Deep Learning
#Model Selection
#Bias-Variance Tradeoff
Machine Learning Engineer
•
Technical
•
hard
We want to roll out a new matching algorithm that pairs drivers and riders. How would you design the A/B test? What metrics would you track, and how do you handle network effects (interference) between the control and treatment groups?
#A/B Testing
#Network Effects
#Switchback Testing
#Statistics
Machine Learning Engineer
•
Technical
•
medium
Uber uses deep learning for ETA prediction. If you notice the ETA model is consistently under-predicting travel times during heavy rain, how would you debug and fix this?
#Model Debugging
#Feature Engineering
#Data Drift
#Deep Learning
Product Manager
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Behavioral
•
medium
Describe a situation where you used data to resolve a conflict between two competing product features vying for engineering resources.
#Data-Driven Decision Making
#Prioritization
#Influence
Product Manager
•
Behavioral
•
medium
Tell me about a time you had to drastically alter your product roadmap due to an external factor, such as a sudden regulatory change or a competitor's launch.
#Adaptability
#Stakeholder Management
#Prioritization
Product Manager
•
Behavioral
•
medium
Tell me about a time you strongly disagreed with an Engineering Manager regarding a technical trade-off or launch timeline. How did you resolve it?
#Conflict Resolution
#Cross-functional Collaboration
#Empathy
Product Manager
•
Coding
•
medium
Given two tables, 'trips' and 'drivers', write a SQL query to find the top 5 cities with the highest driver cancellation rates over the last 30 days, considering only cities with at least 100 total trips.
#Joins
#Aggregations
#Filtering
Product Manager
•
System Design
•
hard
Design a system to track driver locations in real-time. How would you balance the need for high-frequency location updates with the constraint of driver mobile battery drain?
#Mobile Architecture
#Geospatial Data
#Optimization
Product Manager
•
System Design
•
hard
Design the high-level architecture for Uber's surge pricing calculation engine. How do you handle real-time demand and supply spikes?
#Scalability
#Real-time Processing
#Microservices
Product Manager
•
System Design
•
medium
Design the backend architecture for Uber Eats restaurant menu ingestion. How do you handle thousands of restaurants updating their menus, prices, and item availability concurrently?
#Data Ingestion
#Consistency
#API Design
Product Manager
•
Technical
•
medium
What metrics would you define to evaluate the success of the Uber One subscription program, and how would you determine if it is cannibalizing standard rides?
#KPIs
#Cannibalization
#LTV/CAC
Product Manager
•
Technical
•
medium
How would you improve the rider pickup experience at complex, high-traffic venues like major airports or sports stadiums?
#User Experience
#Pain Points
#Innovation
Product Manager
•
Technical
•
hard
You notice that the global ETA accuracy for Uber rides has degraded by 5% over the past week. Walk me through your debugging process.
#Root Cause Analysis
#Machine Learning Metrics
#Debugging
Product Manager
•
Technical
•
hard
Design a ride-sharing service tailored specifically for children and teenagers (Uber for Kids). What are the key features, and how would you address safety?
#Product Design
#Safety
#User Personas
Product Manager
•
Technical
•
hard
Uber is considering acquiring a regional grocery delivery startup versus building the capability natively within the Uber Eats app. How would you evaluate this build vs. buy decision?
#Build vs Buy
#Market Expansion
#Financial Modeling
Product Manager
•
Technical
•
medium
You are the PM for Uber Eats. You notice a 10% week-over-week drop in completed orders in New York City. Walk me through how you would investigate the root cause.
#Root Cause Analysis
#Metrics
#A/B Testing
Product Manager
•
Technical
•
hard
We are considering launching a 'Favorite Driver' feature, allowing riders to request a driver they've previously rated 5 stars. What are the pros, cons, and edge cases of this feature?
#Marketplace Dynamics
#Edge Cases
#Product Strategy
Product Manager
•
Technical
•
hard
Uber wants to expand its footprint in the B2B space with 'Uber for Business'. What should be our go-to-market strategy for enterprise clients?
#B2B Strategy
#Go-to-Market
#Enterprise Solutions
Software Engineer
•
Behavioral
•
hard
Describe a production outage you were involved in. What was the root cause, how did you mitigate it, and what steps did you take to ensure it never happened again?
#Incident Management
#Post-mortems
#Reliability
#System Architecture
Software Engineer
•
Behavioral
•
medium
Uber moves fast. Tell me about a time you had to deliver a project with a very tight deadline. What technical tradeoffs did you make, and would you make the same choices today?
#Time Management
#Technical Debt
#Prioritization
#Agile
Software Engineer
•
Behavioral
•
medium
Tell me about a time you disagreed with a senior engineer or manager on a technical design. How did you resolve the conflict and what was the outcome?
#Conflict Resolution
#Communication
#Collaboration
Software Engineer
•
Coding
•
medium
Given an array of driver locations represented by [x, y] coordinates and a rider's location, find the k closest drivers to the rider.
#Heaps
#QuickSelect
#Geometry
#Sorting
Software Engineer
•
Coding
•
hard
Implement an autocomplete system for Uber's destination search. Given a list of historical destination strings and a search prefix, return the top 3 most frequently searched destinations matching the prefix.
#Trie
#Heaps
#String Manipulation
#Design
Software Engineer
•
Coding
•
hard
Given a list of bus routes where each route is a list of bus stops. Return the minimum number of buses you must take to travel from a source stop to a destination stop.
#BFS
#Graphs
#Hash Maps
Software Engineer
•
Coding
•
medium
Given a grid representing a city map where 0 is a valid road, 1 is a traffic jam (impassable), and a start and end coordinate, find the shortest path for an Uber driver to reach the destination.
#BFS
#Shortest Path
#Graph Theory
#Matrices
Software Engineer
•
Coding
•
medium
Implement a Rate Limiter. Given a user ID and a timestamp, return true if the request should be allowed, and false if it exceeds the limit of N requests per M seconds.
#Sliding Window
#Queues
#Concurrency
#Hash Map
Software Engineer
•
Coding
•
medium
You are given a list of overlapping ride requests, where each request has a start time and end time. Write a function to merge all overlapping ride times and return the consolidated active driving periods.
#Arrays
#Sorting
#Intervals
Software Engineer
•
Coding
•
medium
Design and implement an LRU (Least Recently Used) Cache. It should support get and put operations in O(1) time complexity.
#Hash Map
#Doubly Linked List
#Data Structures
Software Engineer
•
System Design
•
hard
Design a geospatial index to quickly find the nearest available Uber drivers for a rider requesting a trip in a high-density city like New York.
#QuadTrees
#Geohashing
#Spatial Databases
#Scalability
#Redis
Software Engineer
•
System Design
•
hard
Design the backend architecture for Uber Eats, focusing specifically on the order state machine and how to handle real-time updates to the customer, restaurant, and courier.
#Microservices
#WebSockets
#State Machines
#Event-Driven Architecture
#Pub/Sub
Software Engineer
•
System Design
•
hard
Design a distributed logging and monitoring system for Uber's microservices that can ingest millions of logs per second and provide real-time alerting.
#Elasticsearch
#Kafka
#Log Aggregation
#Observability
#Data Pipelines
Software Engineer
•
System Design
•
hard
Design Uber's dynamic pricing (surge pricing) system. How would you handle the data ingestion of millions of location pings and calculate the surge multiplier in real-time?
#Stream Processing
#Kafka
#Apache Flink
#Distributed Caching
Software Engineer
•
Technical
•
hard
Explain how you would implement a thread-safe task scheduler in Go or Java that can execute delayed tasks (e.g., sending a receipt 5 minutes after a trip ends).
#Multithreading
#Priority Queues
#Locks
#Mutexes
#Goroutines
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