Hello everyone, I am a CS student who just graduated from a Top 30 master's degree in the United States this year, majoring in systems and distribution. I previously worked as a back-end intern at a domestic unicorn for one and a half years, and then worked as an SDE Intern at a medium-sized SaaS company in the United States for 6 months. Recently votedSDE jobs at Uber, it only took less than three weeks from submission to getting the offer, and the process moved forward very quickly. The Uber SDE interview process is progressing very quickly, especially in the overseas New Grad and Junior SDE directions, which has picked up significantly compared to last year. Several friends around me also received OA or interview invitations one after another, but the pass rate was not high and they were mainly stuck in the VO stage.
I recorded the entire interview process in detail - the complete details of OA, interview, and VO - hoping to give some reference to students who are preparing for Uber.
Basic information
Position: SDE (Software Development Engineer) Process: Delivery → OA → Side → VO Total cycle: about one week
One side (45 minutes)
After introducing myself, the interviewer asked directly: Do you understand Uber’s business lines and engineering culture? This question may seem simple, but it actually tests your serious preparation. It is recommended to focus on the engineering challenges (high concurrency, low latency, geographical location calculation) behind scenarios such as supply and demand matching, real-time scheduling, and rider delivery. Being able to talk about the engineering level will obviously give the interviewer extra points.
Microservices vs Monolith
It’s a classic question from big companies, but Uber’s question is particularly targeted (because they are a typical case of splitting from monolith to microservices).
Core answer ideas: Don’t memorize the list, talk about trade-off. In the early days, monolith was fast to develop and easy to debug; after the scale expanded, the advantages of independent expansion, independent deployment and fault tolerance of microservices became more important, but it also brought new challenges such as distributed complexity, service governance and link tracking. Enough about this level.
Resume Digging (Core Part)
The interviewer focused on asking questions about two experiences on the resume: high-concurrency processing scenarios and API latency optimization.
- High concurrency: It is necessary to explain clearly what solutions were used (caching, message queue, horizontal expansion, etc.) and give specific results (QPS improvement data).
- API latency optimization: Let’s first talk about how to locate bottlenecks, and then talk about specific optimization methods (N+1 query, gRPC, cache, etc.). Be sure to include numbers.
Important reminder: Your resume must be specific. Only with numbers can you have room for further questions.
Top 3 Technical Challenges
This is the final question. It is recommended to choose really difficult experiences (data inconsistency, distributed lock race conditions, memory leaks, etc.).
Answer structure: Background → What are the difficulties (the most important) → Solution process → Results + review. Many people jump directly to the solution. The interviewer actually wants to hear your understanding of the "difficulties".
Closing question
Finally, I asked about long term goal, why I chose Uber SDE, and what I hope to learn at Uber. The interviewer also briefly introduced the tech stack and daily work in the group. Answers should have specific directions and reflect true motivation.
VO (3 hours and 15 minutes, three consecutive games)
VO is three back-to-back exams, testing Coding, System Design and Behavioral respectively. It lasted nearly three and a half hours, and the stability of physical strength and condition itself was also part of the examination.
Session 1: Coding
The topic is about graph theory or interval class, combined with the taxi-hailing business scenario, similar to the Merge Intervals variant or the BFS shortest path variant. Uber likes to apply algorithms to actual business, such as optimizing the path of drivers taking orders and merging multiple order time windows.
After getting the question, don’t rush to write the code. The first step is to translate the business language into a data structure language - in the taxi-hailing scenario, the "location" is the node, the "road segment" is the edge, and the "time window" is the interval. Once the translation is clear, the questions will return to the algorithmic framework you are familiar with.
Halfway through, the interviewer will ask: Do you tend to optimize for time complexity or space complexity? This question tests your judgment on business scenarios. The taxi-hailing system has extremely high requirements for real-time performance. It is more reasonable to give priority to time complexity, but the key is to be able to explain the reasons rather than just saying them casually.
A detail that is easily overlooked: the cleanliness and readability of the code are very important here. Variable naming, function splitting, and comment logic will all affect the interviewer's judgment of your engineering qualities. One hour is not too tight, leaving 10 minutes to go through the boundary conditions is more valuable than rushing to write an optimized version.
Session 2: System Design
This session started with a deep dive into the resume, and asked about specific plans for microservice disaster recovery, as well as QPS and latency indicators in actual projects. This part must be able to give numbers. It cannot just say "a fuse is used". It must be able to explain clearly how to set the fuse threshold, what the fallback logic is, and how to monitor recovery.
Geographical index selection The core test point is the advantages and disadvantages of GeoHash vs QuadTree. GeoHash encodes longitude and latitude into strings, and the same prefix represents adjacent areas. It is simple to implement and compact in storage. However, in border areas, there will be a problem of greatly different coding of adjacent locations, which requires additional processing. QuadTree recursively divides the map into four equal parts and can adapt to the data density. The accuracy is higher in dense areas such as the city center, but the implementation is complex and the cost of dynamic update is high. It would be a bonus if you can mention Uber's self-developed H3 hexagonal geo-index - but don't mention it if you don't understand the principle.
Driver location update system design This is a core design question. Millions of drivers report their locations every few seconds, and high-frequency writing is a core challenge. The idea is to use advanced Kafka buffering for write operations, batch writes to Cassandra on the consumer side, cache real-time locations of hotspot areas in Redis, and archive historical trajectories to cold storage regularly. Each layer of this link must be able to clearly explain why this component was selected and what problem it solved.
Response to morning peak traffic surge Involves Rate Limiting and peak clipping. Rate Limiting uses token buckets, which can set limits based on driver or regional granularity, and dynamically adjust the threshold during peak periods. The core of peak shaving is to send the traffic to the message queue first, and then the downstream processes it according to its own consumption capacity, without being directly under pressure. At the same time, in conjunction with circuit breakers and downgrades, non-core functions actively give way during peak periods to ensure the stability of core scheduling links.
Game 3: Behavioral & Deep Dive
Production environment SEV incident debugging This question does not test the severity of the accident, but your clarity of thinking under pressure. The order of answering: phenomenon → rapid location → temporary hemostasis (rollback or hotfix) → root cause analysis → complete repair → review mechanism. The first priority is to temporarily stop losses, rather than directly finding the root cause. This detail is easily overlooked.
Time to launch vs code quality trade-off There is no standard answer to this question. The mature answer is to first talk about the factors that affect decision-making (business deadline hardness, severity of technical debt, team resources), then give judgments in specific scenarios, and finally emphasize: You can compromise in the short term, but you must have a fulfillment plan for recording issues, scheduling sprints, and defining acceptance criteria. Avoid both extremes.
How to deal with disagreements with PM or colleagues The most common mistake is to portray yourself as "always right." Effective framework: First confirm whether the goals of both parties are consistent (usually the goals are consistent, and the differences lie in the path) → Use data or user feedback to speak → If there are still differences, propose a small-scale experimental verification → Respect the final decision and implement it completely.
Plan for the next five years This question is relatively open. The key is to have a specific direction and don't say "hope to continue to grow." You can talk about technical depth, upward management, or specialization in a certain field, as long as the logic is consistent.
Core tips for passing interviews with big companies efficiently
I was able to pass smoothly this time, largely due to seeing the latest Uber real questions in the resources compiled by Programhelp in advance. The interview experiences from recently passed candidates, especially the detailed records of the three VO sessions, directly helped me avoid many misunderstandings in the preparation direction. Compared with blindly groping on your own, this kind of targeted resources and guidance can indeed save you a lot of detours. They also provide OA practical assistance, mock interview training and VO real-time idea guidance , which is very helpful for interviews that are tight on time and under high pressure.
If you are also preparing for an Uber interview, my advice is:
- The resume should be realistic, each item should be based on real experience, and numbers and technology choices should be clearly explained.
- The system design requires special preparation, focusing on the high concurrency, real-time and geographical location of the taxi business.
- Behavioral Prepare real cases in advance, logic is more important than rhetoric
- Make more use of the latest interview resources. Information gaps are very critical in quantitative/big factory interviews.
I wish you all can get your favorite Offer. Come on!
