YouTuber Interview Tips: Questions Answers & OA Interviews
As a global leader in transforming mobility and distributionUber (Internet company) The recruitment process is also dynamic and challenging. The entire journey usually starts withResume ScreeningTo start, there is a strong emphasis here on whether you demonstrate skills and experience relevant to the position. Based on internal feedback, Uber places a particular emphasis on demonstrating clarity and specific contributions from past roles on your resume.
After passing the resume screening, qualified candidates may be invited to complete theOnline Testing (OA). This session focuses on your programming and problem-solving skills, and depending on the position you're applying for, it can sometimes involve system design questions. The good news is that the pass rate for this round is said to be around 60%!
After OA, the next step isOne or two rounds of phone interviewsup. These initial phone calls were made primarily through a shared programming platform thatreal swords, real spearsExamine your technical skills, including real-time programming problem solving and algorithm-related questions. Each round of phone interviews usually lasts about 45 minutes. This is aextremely importantstage of the process, the success of the onsite depends on it! So.fully prepared, especially common data structures and algorithms that areKeys to Success. The interviewer will also take this opportunity to assess your problem-solving ideas and communication skills.
If the phone interview was excellent, congratulations, you finally waited for theHardball - on-site interviewsUber's onsite typically consists of three to five rounds of interviews covering programming, system design, and behavioral assessments. Each round is designed to look at your problem-solving skills and to see if you arefit Uber's team culture and whether it can effectively respond to real-world challenges.
Interviews are usuallyface to face, often with whiteboard programming and interactive design exercises. You will encounter at leastA programming question, a system design task, and a behavioral interviewThese will be a comprehensive examination of your algorithmic skills, architectural skills, and teamwork and communication skills. The overall difficulty is moderate to difficult, not only technical hard power, your way of thinking and communication and collaboration is equally important.
Job Type
SDE
Responsibilities:Responsible for the design, development, testing, and maintenance of Uber products and services across multiple domains including front-end, back-end, mobile, and full-stack, with a commitment to building scalable, high-performance, and high-availability software systems and solving complex engineering challenges.
Interviews examine technical points: data structures and algorithms, system design, front-end frameworks, distributed systems, databases, mobile development frameworks and programming language skills
data scientist
Responsibilities:Analyze user behavior, product performance, and market trends, and develop, deploy, and evaluate machine learning models to drive product improvements and business decisions.
The interview examines technical points: statistics and probability, theory and practice of machine learning algorithms, SQL and Python/R programming for data processing and analysis, communication skills and product thinking.
MLE
Responsibilities:Translate machine learning models from concept to reality, deploy algorithms and models to production environments to ensure performance, scalability and reliability.
Interviews look at technical points: ability to productize machine learning models, distributed systems, big data processing techniques, and MLOps practices for model deployment, monitoring, and optimization. Programming skills (especially Python) and system design.
SE
Responsibilities:Design and implement security frameworks, manage vulnerabilities and threats, and define and enforce security standards.
Interviews examine technical points: network security, application security, cloud security, security programming and automation skills. Threat modeling, vulnerability management and to relevant security tools.
The Key Areas of The Interview
YouTubers have difficult interviews, so being proficient in thedual-pointer,breadth-first searchrespond in singingdynamic programmingCrucial because these are frequent test points in their programming questions. Unlike other companies, YouTuber puts a special emphasis on interviews withlook back uponrespond in singingbinary search, they appear more frequently than in a typical programming interview. Therefore, mastering a variety of problem solving methods, including these uncommon strategies, will be the key to interview success.
| Features | Percentage |
|---|---|
| Misc | 7.6% |
| Simulation | 1.3% |
| Two Pointers | 10.1% |
| Adv. Data Structure | 16.5% |
| Backtracking | 3.8% |
| Basic Data Structure and Algorithm | 6.3% |
| Binary Search | 8.9% |
| Heap | 7.6% |
| Graph | 7.6% |
| Dynamic Programing | 5.1% |
| Depth-First Search | 12.7% |
| Breadth-First Search | 12.7% |
At YouTuber, the programming interview questions are generally difficult, sometimes even more complex than those at Google or Meta. Overall, interviews at YouTubers are difficult, but in line with the standards of most FAANG companies, and maybe even much harder than Amazon's.
If you look at the percentage of difficulty of the questions, only 7.41 TP6T were easy questions, the percentage of medium difficulty questions was as high as 51.91 TP6T, and the remaining 40.71 TP6T were high difficulty interview questions.
Questions and Difficulty
| Common Interview Questions | Examining technical points | degree of difficulty or ease |
|---|---|---|
| public transport line | breadth-first search | challenging |
| Next number of palindromes using the same number | dual-pointer | challenging |
| quadtree | sundry | medium difficulty |
| Find the closest echo | Basic Data Structures and Algorithms | challenging |
| Construct K palindromes | Basic Data Structures and Algorithms, Miscellaneous | medium difficulty |
| Number of islands II | Advanced Data Structures | challenging |
| slide puzzle | breadth-first search | challenging |
| Bomb Enemy | dynamic programming | medium difficulty |
| Optimal account balance | Backtracking algorithms, dynamic programming | challenging |
| Columns with at least one 1 on the leftmost side | binary search | medium difficulty |
Uber OA (online assessment)
The Superior Online Assessment (OA) is designed to initially screen technical candidates, looking primarily atData Structures and AlgorithmsFeats, including common structures such as arrays, chained lists, trees, graphs, stacks, queues, and hash tables, as well as sorting, searching (especiallybinary search),dynamic programming,look back upon,BFS/DFSand other algorithms, usually difficult LeetCode-style programming questions, and sometimes system design (for senior positions). Candidates need to focus on preparing for the YouTubers' preferredlook back uponrespond in singingbinary search, and focuses on time management and code optimization to ensure efficient problem solving and passing performance tests in limited time.
SDE
Examine the technical points:
- Data Structures & Algorithms. Arrays, chained lists, trees, graphs, hash tables, stacks, queues, heaps, sorting, searching, dynamic programming, greedy algorithms, etc. Proficiency in common algorithmic ideas and the ability to write efficient, bug-free code.
- System Design. Distributed system design, scalability, reliability, performance optimization, API design, database selection, message queuing, caching strategies, etc. Ability to design a large-scale system from scratch and discuss its advantages and disadvantages.
- Object-Oriented Design (OOD). Good class design, inheritance, polymorphism, interfaces, design patterns, etc.
- Programming Language. Proficient in at least one major programming language (e.g. Python, Java, C++, Go, etc.) with an understanding of its features and best practices.
- Problem Solving. Ability to clearly understand problems, break them down, propose multiple solutions, and weigh the pros and cons.
Interview Questions:
- Algorithmic Questions: Implement a function to find all pairs of two numbers in an array whose sum is a given target value (Two Sum). Requires optimization of time complexity.
- Data Structure Questions: Design and implementation of a "parking lot" data structure, support for vehicle entry, exit, query empty space and other functions.
- System Design Questions: How to design an Uber-like taxi service system? Please discuss the core components, data flow, scalability challenges, and solutions.
- OOD Question: Design an elevator system. Consider elevator operation logic, passenger requests, and coordination between multiple elevators.
- Behavioral/Project Experience Question: Describe one of the most challenging technical problems you've ever encountered and how you solved it? Or, talk about how you handled risk and failure on a large project.
data scientist
Examine the technical points:
- Statistics & Probability. Hypothesis testing, A/B testing, regression analysis, analysis of variance, probability distributions, confidence intervals, etc. Understand statistical concepts and their application to practical problems.
- Machine Learning. Supervised Learning, Unsupervised Learning, Classification, Regression, Clustering, Model Evaluation (Precision, Recall, F1 Score, ROC Curve, etc.), Feature Engineering, Model Selection, Over/Underfitting, Common Algorithms (Linear Regression, Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, SVM, Neural Networks, etc.).
- Data Manipulation & SQL. Proficient in using SQL for data querying, aggregation, joins, etc. Familiar with big data processing tools (e.g. Spark).
- Programming. Proficiency in data analysis and model development using Python or R, including data visualization libraries, scientific computing libraries (Pandas, NumPy, Scikit-learn).
- Product Sense & Business Acumen. Ability to translate data insights into business decisions, understand product metrics, and design experiments to measure product characteristics.
- Communication. Ability to clearly explain complex statistical and machine learning concepts to non-technical people.
Interview Questions:
- Stats/A/B test questions: Uber has gone live with a new feature, how do you design an A/B test to measure its impact on user conversions? What metrics would you focus on, and are A/B test results with a p-value of 0.04 valid?
- SQL Question: Let's say you have the rides table (with fields like user_id, driver_id, distance, fare, status, timestamp, etc.) and the users table (with fields like user_id, registration_date, etc.).
- Write a SQL query that calculates the average fare per city and excludes canceled orders.
- Write a SQL query to find drivers who have completed more than 5 trips in the past month, listing their names and total number of trips.
- Machine Learning Questions: If you were to predict the demand for Uber rides, what characteristics would you consider? What model would be chosen? How would you evaluate model performance? How would you handle peak and trough demand forecasting?
- Product analysis questions: How would you measure the success of Uber Eats' new recommendation algorithm? What metrics would you design to track its effectiveness?
- Conceptual/Theoretical Questions: Explain what the Bias-Variance Tradeoff (BVT) is and how to deal with it in practical model development. How to deal with unbalanced datasets?
MLE
Examine the technical points:
- Machine Learning Theory (ML Theory). In-depth understanding of the principles, strengths and weaknesses, and applicable scenarios of various machine learning algorithms, including deep learning, reinforcement learning, etc.
- Model Development & Optimization. Feature engineering, model training, hyperparameter tuning, model deployment, A/B testing, model monitoring.
- ML System Design. Design of scalable and highly available ML production systems, including data pipelines, model services, feature storage, online/offline prediction architectures, etc.
- Programming. Proficiency in Python, knowledge of ML frameworks (TensorFlow, PyTorch), familiarity with distributed computing frameworks (e.g. Spark, Ray).
- MLeOps/Productionization. Familiar with model deployment, version control, monitoring, logging, rollback, and other production environment practices.
- Big Data Technologies. Familiarity with Hadoop, Spark, Kafka and other big data toolchains.
Interview Questions:
- ML System Design Questions: How to design a system for predicting Uber hailing arrival time (ETA) in real time? Please consider data sources, model selection, feature engineering, service architecture, and how to handle real-time requirements.
- Algorithm/Modeling Questions: Explain the differences and connections between Gradient Boosting and Random Forest and in which scenarios they are more applicable.
- MLeOps Question: How do you monitor the performance of a machine learning model deployed to a production environment? How would you troubleshoot and resolve issues when model performance degrades?
- Coding/algorithmic questions (possibly with ML background): Design a function to recommend drivers to passengers, taking into account factors such as distance, driver ratings, and historical passenger preferences. Discuss the optimization strategy for your matching algorithm.
- Conceptual/Practical Questions: How do you solve the cold start problem when building a recommender system?
SE
Examine the technical points:
- Network Security. TCP/IP protocols, firewalls, IDS/IPS, VPN, DDoS protection, OSI model, network scanning, common network attacks (e.g., SYN Flood, ARP Poisoning).
- Application Security. OWASP Top 10, Web Application Vulnerabilities (XSS, SQL Injection, CSRF, SSRF), API Security, Authentication and Authorization Mechanisms (OAuth, JWT, SAML), Secure Coding Practices.
- System Security. OS Security (Linux/Windows), Container Security (Docker, Kubernetes), Virtualization Security, Endpoint Security, Patch Management.
- Cryptography. Symmetric encryption, asymmetric encryption, hash functions, digital signatures, TLS/SSL protocols, key management.
- Compliance & Risk Management. Familiarity with common security standards and frameworks (ISO 27001, NIST), risk assessments, security audits.
- Cloud Security. Familiarity with security best practices and security services for cloud platforms such as AWS, Azure, GCP, etc.
- Incident Response. Event detection, analysis, containment, eradication, recovery.
- Programming & Automation. Ability to write security tools, automate security tasks using scripting languages (e.g. Python, Go, Shell).
Interview Questions:
- Apply security questions: How to prevent SQL injection and cross-site scripting (XSS) attacks? Please give specific defense strategies and code examples.
- Cybersecurity Questions: Explain the principles and common types of DDoS attacks and how to design a system to defend against large-scale DDoS attacks.
- System Security Question: When you find a server compromised in a production environment, what steps do you take to respond and contain the threat?
- Safe Design Questions: How to design a secure microservice architecture? Please discuss security considerations for inter-service communication, authentication, authorization, data encryption, etc.
- Programming/scripting questions (possibly with a security background): Given a web application log file, write a script that identifies and reports potentially malicious behavior in it (e.g., multiple failed login attempts, unusual HTTP request patterns).
Behavior Questions
Classical Behavioral Problems
1. Tell me about a time when you worked with a team to solve a difficult problem.
In your response, describe in detail the specific problem encountered and emphasize the importance of teamwork. Describe your role in the team, the strategies the team adopted, the tools and techniques used, and the end result. The key is to demonstrate your ability to work harmoniously with the team and to respond effectively to complex and time-critical problems.
2. describe an experience where you had to learn new technology quickly to complete a project.
Detail your learning process for adapting to new technology under pressure. Include how you assessed the skills needed, the method of learning, and how you applied this new technology. Demonstrate your ability to learn quickly and be flexible by emphasizing successes that resulted from rapid adaptation.
3. Give an example of how you would handle multiple tasks at the same time.
Explain your background in managing multiple tasks and your strategies for staying organized and ensuring that all tasks are completed on time. Highlight your time management and prioritization skills and how they help you successfully deliver on commitments without sacrificing the quality of your work.
Teamwork behavioral issues
1. Describe an experience where you worked with a team on a project that failed or did not meet expectations. What role did you play? How did you respond to the situation?
Focuses on the constructive approach you take when a project is frustrated. Emphasizes your communication skills, your role in dealing with specific issues, and how you can help identify problems and propose practical solutions to improve results.
2. Tell us about a successful project in which you were involved. What was your contribution? How did you ensure effective collaboration between team members?
Emphasize your ability to work with others and detail your contribution to the success of the project. Mention how you facilitated communication between different team members to ensure that everyone had the same goals and completed tasks on time.
3. Superior prides itself on innovation. Can you share an experience where you worked with a team to innovate a process or product?
Tell us about a project you were involved in that required innovative thinking. Describe the collaborative process, your specific contribution (especially if you came up with an idea), and the impact of the innovation on the project goals or company goals.
Post-specific behavioral issues
1. Describe a challenging software development project you have worked on. What were the main obstacles? How did you overcome them?
Focus on the problem solving skills you utilized, the specific techniques applied, and the impact of the solution. Mention teamwork and the innovative approaches taken to deal with unexpected problems during the project.
2. Can you give an example of a time when you had to learn new technology to complete a project? How did you respond to this challenge?
Emphasize your ability to adapt and learn quickly. Discuss strategies for mastering new technologies, such as online courses, reading documents, or collaborating with knowledgeable colleagues. Emphasize how this learning curve can benefit a project or team.
3. Ubiquitous emphasizes high-performance software. Can you share an experience where you improved the efficiency or performance of your system?
Discuss a specific case where performance improvement was critical. Explain the diagnostic process, the changes implemented, and the results achieved. If possible, use metrics or data to quantify the effect of the improvement. And relate this to the importance of efficiency in a fast-paced company like YouTuber.
Interview Prep
YouTuber's engineer interviews are notoriously challenging, and candidates need to make thorough and in-depth preparations, brushing up on LeetCode and online mock interviews are essential, especially through mock interviews and taking the initiative to ask for advice on interview details and interview questions from senior executives in equivalent positions in large factories.
In the technical side of the session.Data Structures and Algorithms (DSA) is the core, candidates must be proficient in common data structures such as arrays, chained lists, trees (binary, balanced, etc.), graphs, stacks, queues, hash tables, etc., and be proficient in various algorithms such as sorting (quick sort, merge sort), searching (binary search(BFS, DFS),dynamic programming,look back upon,dual-pointeretc. YouTubers pay special attention tolook back uponrespond in singingbinary search, and therefore need to be practiced more intensively in these two areas. Also, the ability to analyze the time and space complexity of algorithms is crucial.
In addition to the algorithm.system designIt is also a top priority for interviews for Senior Engineer positions. This requires the candidate to be able to design scalable, highly available, and highly concurrent distributed systems involving knowledge points such as microservices architecture, database selection (SQL/NoSQL), caching, load balancing, message queuing, API design, and CAP theory.
