Yelp, a leading local lifestyle platform, is a sought-after choice for international students. Yelp VO interview, testing technical skills and cultural fit, is tough. ProgramHelp has helped hundreds land big company offers. Here, we dissect Yelp VO, share real questions, and give practical prep tips to clarify key assessment points and guide your preparation.
Yelp VO Process
- Initial Screening: HR reviews resumes, focusing on experience in recommendation systems, search, or data analysis, such as optimizing user review functions. A 30 – minute phone call to discuss background, motivation, and salary expectations.
- OA: 60 – 90 minutes on HackerRank, with 2 – 3 algorithm questions.
- Phone Interview: 1 – 2 rounds of 45 minutes each, using CoderPad to solve algorithm problems while explaining the thinking process aloud.
- VO : 4 – 6 rounds, including coding, system design, behavioral and cultural interviews, lasting approximately 4 hours, possibly with an executive interview. Maintain clear logic and show enthusiasm for Yelp’s mission.
Preparation for Yelp VO
The Yelp VO interview typically consists of 4 – 6 rounds, covering coding, system design, and behavioral questions, lasting about 4 hours, with the possibility of an executive interview.
- Technical Positions: Need to practice LeetCode medium – level questions (arrays, strings, graphs), be familiar with HackerRank and CoderPad, and practice explaining code while writing.
- System Design: Master the architecture of search or review systems, and learn tools like ElasticSearch and Redis.
- Behavioral Questions: Prepare 2 – 3 stories using the STAR method, highlighting Yelp’s values of “connecting people with local businesses”, such as a user – centered approach.
- Pre – interview Preparation: Test the network and microphone in advance to ensure a stable VO environment. During the interview, ask questions (e.g., “How does Yelp improve the credibility of business ratings?”) to show enthusiasm.
Real Yelp VO Questions
Algorithm Question: Restaurant Rating Sorting
Problem Description: Given a list of restaurants (each with a rating and the number of reviews), return the restaurants sorted in descending order of rating. If the ratings are the same, sort by the number of reviews in descending order.
Example:
Input: [{name: “Cafe”, rating: 4.5, reviews: 100}, {name: “Bistro”, rating: 4.5, reviews: 200}, {name: “Diner”, rating: 4.0, reviews: 50}]
Output: [“Bistro”, “Cafe”, “Diner”]Solution Idea: Use Python’s sorted function with a custom sorting key to compare ratings and review counts. Time complexity: O(n log n).
def sortRestaurants(restaurants):
return [r["name"] for r in sorted(restaurants, key=lambda x: (-x["rating"], -x["reviews"]))] System Design Question: Yelp Review System
Problem Description: Design Yelp’s review system to support user submission, viewing of reviews, handle millions of data, and ensure high concurrency and low latency.
Example: When a user submits a review, the system updates the restaurant’s rating in real time and sends notifications.
Solution Idea:
- Architecture: Use MySQL for storing reviews (sharding for scalability), Redis for caching popular reviews, and Kafka for processing asynchronous notifications.
- Process: User submits a review → API gateway validation → write to MySQL → update Redis → Kafka triggers notifications.
- Key Points: Use distributed locks to prevent concurrency conflicts and optimize queries with indexing. Time complexity: O(1) for writing, O(log n) for querying.
Behavioral Question: Enhancing User Experience
Problem Description: Yelp’s core mission is “connecting people with local businesses”. Share an experience where you improved user experience through technology or collaboration.
Reference Answer (STAR):
- Situation: Users reported inaccurate search results, affecting the experience of discovering restaurants.
- Task: Optimize the search function to improve user satisfaction.
- Action: Analyzed user behavior data, optimized the weights of the recommendation algorithm, and coordinated with the front – end team to improve UI feedback.
- Result: Search relevance increased by 15%, and user retention rate rose by 10%.
Say Goodbye to Interview Worries and Secure Your Dream Offer!
The ProgramHelp team, with a hands – on professional spirit, provides customized services such as interview proxy, interview assistance, and remote interview support. Whether it’s exam proxy, code writing, or assignment consultation and tutoring, we help you easily enter famous enterprises. Don’t wait any longer, take action now!
