ByteDance Interview Tips: Questions, Answers & OA Interviews

TikTok Interview Prep: Questions & OA Walkthrough

Tiktok OA Interview Experience - Jitterbug Interview Aid

The hiring process for TikTok (BytesDance) typically begins withRigorous resume screeningThe recruiting team will have a keen eye for evaluating your background and key skills for your desired position.height-related, especially mobile or platform engineering experience would be the focus. It is said that only about 20% of applicants can pass this hurdle and move on to the next stage.

After successfully passing the resume screening, you will receiveOnline Testing (OA) This is an invitation that focuses on your programming and problem solving skills. And after OA, there are usually only about 10% Participants of the program are able to enter an initial phone call, which assesses your cultural fit and communication skills.

If successful through initial communication (or that'sReal Phone Screeningstarted), there will usually be one or two rounds of technical phone interviews. These interviews focus on assessing your technical skills and suitability for the role, and will cover programming challenges and problem-solving related questions. It's also a chance for you to demonstrate your understanding of the TikTok tech stack and think about how you can contribute to existing projects.good opportunity. Can we leave it here?Impressive first impressionFor those who make it to the on-site interviewextremely importantThe

Pass the phone interview and congratulations on your arrival atThe most exciting and challengingTikTok's live interview session!It's very fast-paced.This is usually done in theConsecutively in one dayFour to five rounds, intensityhigh. Content of the interviewVery comprehensive and in-depth, covering programming, system design, and behavioral assessment, each round is carefully designed toomni-directionalEvaluate your technical prowess and whetherA true fit.Team culture.

Get ready to be inShowing off your skills on the whiteboardWrite code! There will also be an in-depth discussion on system architecture, as well as behavioral questions to understand your problem-solving mindset and teamwork skills. Overall, this is an intense, fast-paced interview test designed to confirm that you have what it takes to make a difference at a company like TikTok!High speed and full of innovationThe potential to thrive in the environment of the

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Job Type

Backend Development Engineer

Job responsibilities: design, develop, and maintain high performance, high availability, and high concurrency distributed systems.

Interviews examine technical points: programming languages, data structures and algorithms, operating system basics, networks, databases, system design, etc.

Front-end Development Engineer

Responsibilities:Responsible for the development and optimization of TikTok web application, front-end performance optimization, maintainability and user experience.

Interviews examine technical points: front-end scripting languages, frameworks/libraries, networking, basic data structures and algorithms.

Mobile Engineer

Responsibilities:Responsible for the development, maintenance and optimization of TikTok iOS or Android App.

Interviews examine technical points: iOS, Android, audio and video processing, data structures and algorithms, operating systems.

algorithm engineer

Responsibilities: Design, development and optimization of TikTok's core algorithms, such as recommender systems, content understanding, computer vision, natural language processing, and advertisement placement.

Interviews examine technical points: math fundamentals, deep learning, data processing, programming skills, system design

The Key Areas of Tech Interview

When preparing for TikTok software engineering interviews, it is very helpful to focus on breaking through the high-frequency algorithmic questions in the categories of breadth-first search (BFS) and depth-first search (DFS). At the same time, a thorough understanding and flexible use of advanced data structures will emphasize your strengths and demonstrate deep skills in solving complex problems. After all, TikTok, as a top tech company, requires high algorithmic skills from its candidates, and the interview will definitely involve extensive algorithmic tests.

Features	Percentage
Misc	8.9%
Simulation	1.1%
Two Pointers	5.6%
Adv. Data Structure	13.3%
Backtracking	3.3%
Basic Data Structure and Algorithm	5.6%
Binary Search	7.8%
Heap	7.8%
Graph	10%
Dynamic Programing	6.7%
Depth-First Search	15.6%
Breadth-First Search	14.4%

Programming questions in software engineering interviews at TikTok are often difficult, but (compared to companies like Google or Meta) not prohibitively difficult. Interviewers will often look at algorithms such as depth-first search (DFS), breadth-first search (BFS), and dynamic programming (DP). While the overall difficulty of the interviews is not bad, they are relatively moderate compared to some of the other FAANG companies, and are designed to be a balanced assessment of the candidate's various software engineering skills.

If you look at the percentage of difficulty of the questions, only 4.31 TP6T were easy questions, the percentage of medium difficulty questions was as high as 63.81 TP6T, and the remaining 31.91 TP6T were high difficulty interview questions.

Questions and Difficulty

Common Interview Questions	Examining technical points	degree of difficulty or ease
Designing an in-memory file system	Advanced Data Structures	challenging
Rearrange the strings at distance k	Pile, miscellaneous	challenging
non-decreasing or non-increasing array	Dynamic programming, miscellaneous	challenging
Optimal account balance	Backtracking, dynamic programming	challenging
Calculate the paths in the tree that can form a palindrome	Depth-first search, dynamic programming	challenging
Pathways and IV	depth-first search	medium difficulty
Number of different islands	Advanced data structures, breadth-first search, depth-first search	medium difficulty
Number of good leaf node pairs	depth-first search	medium difficulty
frog jump	dynamic programming	challenging
Reformatting dates	Basic Data Structures and Algorithms	simpler

TikTok OA (online assessment)

The Online Assessment (OA) for TikTok Engineer positions is a very critical initial screening part of the hiring process. Its main purpose is to efficiently and consistently examine the most basic and important technical skills of candidates when faced with a large number of applicants, especially their mastery of core data structures and algorithms as well as their ability to solve problems through real-world programming. This is like a technical threshold, only the candidates who have demonstrated solid basic skills through OA can have the opportunity to enter the subsequent more in-depth interviews, which greatly improves the recruitment efficiency.

In order to successfully pass TikTok's OA, candidates need to focus on preparing for data structure and algorithm questions, especially strengthening the practice of common data structures such as chained lists, trees, graphs, hash tables, and algorithms such as DFS, BFS, Dynamic Programming, and Greedy, etc. It is recommended that candidates practice more on question-scrubbing platforms such as LeetCode, and familiarize themselves with the types of questions that are moderate and difficult. When participating in OA, in addition to accurately understanding the requirements of the questions, you must pay attention to thinking about various edge cases, writing clear and robust code, and considering its time and space complexity. Effective time management is also very important to ensure that all questions are completed within the allotted time.

Backend Engineer

Examine the technical points: Data Structures and Algorithms, System Design, Distributed Systems Theory and Practice, Database Principles and Optimization, Concurrent Programming, Computer Networks, Specific Language Features, Code Quality and Maintainability.
Interview Questions:
1. How to design a high-performance distributed counter for high concurrency scenarios?
2. Designing a URL Shortener that supports hundreds of millions of users with high concurrency.
3. Explain the CAP theorem and how to make trade-off choices based on business scenarios when designing a distributed caching system?
4. How to design a Follow table in a social application with a large number of users? How to optimize the performance of querying "People I follow" and "People who follow me"?
5. Using a language you are familiar with, implement a thread-safe producer-consumer model.

Frontend Engineer

Examine the technical points: HTML/CSS/JavaScript fundamentals, front-end frameworks (React/Vue/Angular, etc.) in-depth, how browsers work, network requests and optimization, performance optimization, component design and state management, front-end engineering, data structures and algorithms fundamentals.
Interview Questions:
1. Explain the Event Loop mechanism of JavaScript and write code to demonstrate the execution order of setTimeout, Promise, async/await.
2. Design a drag-and-drop sortable list component. Need to consider state management, performance optimization and user experience.
3. How to diagnose and optimize a slow loading page? Please list the troubleshooting steps and possible optimizations.
4. Explain the Same-Origin Policy and how to resolve cross-domain issues (CORS).
5. Designing a routing system and state management scheme for a large single page application (SPA) requires consideration of code segmentation, lazy loading, etc.

Mobile Development Engineer

Examine the technical points: Platform Features (iOS/Android SDK, UI, Lifecycle), Memory Management, Multithreading and Concurrency, Performance Optimization, Network Programming, Audio and Video Processing, Architectural Patterns, Data Structures and Algorithms Fundamentals.
Interview Questions:
1. Explain the Launch Mode of Android Activity and the application scenarios of different modes.
2. Explain the lifecycle methods of an iOS App and the timing of their invocation at different state transitions.
3. How to diagnose and fix memory leaks or overuse issues in your App? List commonly used tools and methods.
4. How to safely and efficiently perform network requests and data processing in mobile apps to avoid blocking the main thread? (Examine GCD/OperationQueue or Coroutines/Threads)
5. Discuss and compare the architectural patterns you are familiar with in mobile development (e.g., MVC, MVVM, MVP, VIPER) and explain the advantages, disadvantages, and scenarios in which they are applicable.
6. If you were to implement a simple video player from scratch, what key technical points and challenges would you consider?

Algorithm Engineer

Examine the technical points: Fundamentals of Mathematics (Probability, Statistics, Linear Algebra), Machine Learning/Deep Learning Principles and Algorithms, Data Mining, Feature Engineering, Model Evaluation and Optimization, Domain Specific Knowledge (Recommendations, CV, NLP, Advertising, etc.), Programming Skills, Algorithmic System Design.
Interview Questions:
1. Explain the principles of Logistic Regression and write its loss function and gradient descent update formula.
2. Design an algorithmic framework for short video recommendation based on users' historical viewing behavior. Recall, sorting, and cold start need to be considered.
3. Explain the role of convolutional and pooling layers in Convolutional Neural Networks (CNN) and their advantages in image processing.
4. How do you design an A/B test to evaluate the effectiveness of a new sorting algorithm in a recommender system? What metrics and potential problems need to be considered?
5. Implementation of the K-Means clustering algorithm.

Behavior Questions

Classic Behavioral Interview Questions and Preparation Ideas

In a behavioral interview session for software engineering, the interviewer will often use classic questions to get a sense of your personal traits, problem-solving mindset, and work style. For example:

"Describe a project in which you used a combination of programming skills and creativity?" When answering these types of questions, focus on how you found a balance between innovative ideas and technical implementation. You can specifically look at the project requirements, your unique approach, the technology stack used, and the final outcome. In particular, emphasize the specific challenges encountered and how your innovative solution directly drove the project to success.
"Tell us about an experience where you had to learn and master a new tool or technique quickly in order to complete a project." With this example, aim to demonstrate your ability to adapt and your strong desire to learn. Articulate a clear background reason for needing to learn quickly, what specific steps you took to learn, and how efficiently you applied your newfound learning to the project. If you can, it's more compelling to mention specific metrics that measured the success of the project or the feedback you received.
"Please share an example of how you have handled conflict within a development team." This question examines your interpersonal communication skills and conflict resolution skills. Describe a specific situation, including your role on the team, the cause and nature of the conflict, and the mediation or resolution you adopted. Highlight any positive improvements in teamwork or project progress after the conflict was resolved.

Teamwork Behavior Interview Questions and Preparation Ideas

Teamwork is crucial in modern software development, so interviewers will also be looking at how you perform in a team environment:

"Describe an experience where you collaborated with multiple teams to complete a project. What role did you play in it? How did you ensure a smooth and successful project?" Highlight your communication coordination and project management skills here. Share a concrete example of how you successfully delivered a project by effectively responding to a complex teamwork challenge and ultimately delivering a successful project. You can emphasize the leadership role or key contributions you made, as well as the innovative approaches you used to overcome difficulties.
"Please provide an example of how you would handle a disagreement that arises within a software development team?" Talk about a scenario that required the use of active listening and empathy. Describe the steps you took to understand different points of view and the strategies used to negotiate or reach a compromise. Focus on the end result, demonstrating that team relationships were strengthened and had a positive impact on the project.
Considering a company like TikTok that values innovation and user engagement, you may also be asked:"TikTok places a strong emphasis on innovation and user engagement. Please share an experience where you found a creative solution to enhance the user experience in a collaborative project." Provide an example where your innovative approach has directly improved user engagement. This could be designing a feature that is intuitive and easy to use, optimizing system performance, or effectively incorporating user feedback into the development process. Emphasize how your collaboration with team members on this project was critical to success.

Job Specific Behavioral Interview Questions and Preparation Ideas

In addition, the interviewer will ask questions based on the characteristics of the position you are applying for:

For example, for a position involving machine learning, one might ask:"Describe a project where you were responsible for implementing a machine learning algorithm. How did you ensure the accuracy and efficiency of the model?" Candidates should describe their approach to selecting, implementing and optimizing machine learning algorithms. Mentioning how data preprocessing, algorithm selection, and parameter tuning were handled can demonstrate their problem-solving skills and their focus on practical results.
The ability to continue learning is important no matter what the position:"How do you continue to learn and keep abreast of the latest trends and technologies in software engineering? How has this knowledge been applied to your previous work?" Answers should demonstrate the applicant's commitment to continuous learning and refinement of skills. Focus on how understanding the latest information has directly impacted previous projects or contributions, especially what new tools or techniques have been used to enhance project outcomes.
Re-emphasize company traits such as:"TikTok puts a lot of emphasis on creating engaging user experiences through innovation. Can you share an experience with a program you developed or worked on that significantly increased user engagement?" This response should focus on a project that involved an innovative solution that effectively increased user interaction or satisfaction. A detailed discussion of the specific changes you made to the project, the logic behind your thinking, and how you measured the increase in user engagement will be a strong response to the company's concerns about the impact of the project.

Interview Prep

Whether you are applying for front-end, back-end, mobile or other technical positions, solid data structure and algorithm (DS&A) skills are the core and most fundamental requirements.TikTok interviews place great emphasis on a candidate's ability to solve complex problems, which is often demonstrated through algorithmic questions. Therefore, systematically reviewing various data structures (e.g., chained lists, trees, graphs, hash tables, etc.) and algorithms (e.g., sorting, searching, dynamic programming, greedy, etc.), as well as practicing a lot of programming on platforms such as LeetCode and familiarizing yourself with common types of problems and problem-solving patterns, are essential parts of the process. This is the basic gateway to subsequent interview rounds.

Second, for the specific position you are applying for, you need to prepare in-depth relevant professional and technical knowledge. For example, if you are a front-end engineer, you need to have an in-depth understanding of JavaScript, HTML, CSS, be familiar with mainstream front-end frameworks (such as React, Vue, Angular), and understand the working principle of the browser, performance optimization and front-end system design; back-end engineers need to be proficient in the development language used, have an in-depth understanding of the basic knowledge of distributed systems, database principles, operating system Back-end engineers should be proficient in the development language used, have an in-depth understanding of distributed systems, database principles, operating systems, network protocols and other basic knowledge, and have good back-end system design capabilities; mobile (Android/iOS) engineers need to master their respective platforms' development languages, UI development, performance tuning, memory management, and unique architectural patterns. When preparing, you should focus on the core requirements in the job description, and study and review them in depth.

For candidates with some work experience, System Design is often a very important part of the interview, especially at a large-scale internet company like TikTok. System Design aims to examine your ability to build a scalable, highly available, and highly concurrent system. You need to learn common system design patterns, components (such as caching, message queuing, database selection, load balancing, etc.), and be able to think, analyze, and weigh technical solutions for specific scenarios (e.g., designing a short-video recommendation system, commenting system, etc.). This component is critical to assessing your architectural skills and ability to solve complex engineering problems.

In addition to technical skills, behavioral interviews and soft skills are also examined.TikTok is looking to recruit engineers with good collaboration skills, communication skills and positive work attitudes. Interviewers will use behavioral questions to learn about your past experiences in handling problems, facing challenges, working with teams, and learning new things. When preparing, you can sort out specific examples from your past projects, use STAR principles (Situation, Task, Action, Result) to clearly organize your answers, and show your problem-solving process and the growth you have gained from it. It's also a plus to show interest and enthusiasm for TikTok's products and culture.

TikTok VO Sample Interview Questions

The interviewer throws out questions: "Design an efficient algorithm to find out how many of the videos 'liked' (liked) by TikTok users were posted by the same creator in the last 24 hours. Suppose we have a massive data stream of user 'like' videos, as well as information about when the videos were posted, and the creator ID. You need to consider the large scale of data and real-time requirements."

The participant frowned in the face of this question. He clearly realized that this was a problem involving big data processing and time windows, but was momentarily at a loss as to how to efficiently process real-time data streams and aggregate statistics. He started to write down some ideas based on traditional database queries in the draft area, but quickly shook his head, realizing that this approach could not meet the real-time and high concurrency requirements at the level of TikTok. He tapped the desktop gently and fell into deep thought.

Just as he paused, we gave a quick reminder in text form remotely: "Consider using a stream processing framework, such as Apache Flink or Kafka Streams." "Consider incremental computation for aggregation within a time window. "

Hearing this hint, he immediately grasped the breakthrough in solving the problem. He resumed tapping up the code in the editor (here I will use pseudo-code to represent the core logic, as the exact implementation will depend on the API of the stream processing framework), explaining it as he wrote:

"For a massive data streaming scenario like TikTok, we can't simply handle it with a relational database. We need a stream processing system to handle the event of a user 'liking' a video in real time. I will consider using Apache Flink to solve this problem. The core idea is:"

"1. Data source access: First, we need to access the event data of users 'liking' videos into Flink.These events will contain information such as user ID, video ID, video posting time, creator ID, etc. This data can be consumed from Kafka topics."

"2. Timestamp Assignment: Video release time is central, so it is necessary to assign event timestamps correctly and handle possible disorganized events.Flink's Watermark mechanism handles this well."

"3. Grouped by creator: Our goal is to count the number of videos from the same creator, so we need to perform a keyBy operation based on creator_id to route events from the same creator to the same task slot for processing."

"4. Sliding time window: We need to count the data over the last 24 hours, which requires the use of a Sliding Event Time Window (SETW). The window size is set to 24 hours and the sliding intervals can be set according to real-time requirements, such as 1 minute or 5 minutes, so that the statistics can be constantly updated."

"5. In-window aggregation: Within each window, we need to count videos from the same creator. Each time a video is 'liked', the counter is added by one. We can maintain a ValueState to store the number of videos from each creator within the current window."

"6. Result Output: Output the results of each window's aggregation (creator ID, number of videos liked in the last 24 hours) to a downstream system, such as another Kafka topic, for use in a real-time dashboard or recommendation system."

Soon, he wrote the following code in the editor:

				
					# Let's say we have a Schema where the user "likes" a video.
class LikeEvent: user_id: str
    user_id: str
    class LikeEvent: user_id: str
    publish_time: int # Unix timestamp
    creator_id: str

# Flink DataStream API pseudo-code
def process_tiktok_likes(env): # 1. consume user "likes" from Kafka.
    # 1. Consume a stream of events from Kafka for user "likes" of a video
    like_events_stream = env.add_source(
        KafkaSource.builder()
        .setTopics("tiktok_like_events")
        .setStartingOffsets(OffsetsInitializer.earliest())
        .setValueOnlyDeserializer(LikeEventDeserializationSchema())
        .build()
    )

    # 2. Assigning Event Timestamps and Watermarks
    # use the publish_time of the video as the event time
    # Allow some disorganization (e.g. 5 seconds)
    watermarked_stream = like_events_stream.assignTimestampsAndWatermarks(
        WatermarkStrategy
        . forBoundedOutOfOrderness(Duration.ofSeconds(5))
        .withTimestampAssigner((event, timestamp) -> event.publish_time * 1000) # converted to milliseconds
    )

    # 3. Group by creator ID
    keyed_stream = watermarked_stream.keyBy(lambda event: event.creator_id)

    # 4. define sliding time window (24 hour window, 1 minute sliding)
    windowed_stream = keyed_stream.window(
        SlidingEventTimeWindows.of(Time.hours(24), Time.minutes(1))
    )

    # 5. Aggregation within the window: counting the number of videos per creator
    # ProcessWindowFunction is used here for more flexible handling of state and context information
    processed_stream = windowed_stream.process(
        ProcessWindowFunction<LikeEvent, Tuple2, String, TimeWindow>() {
            private ValueState creatorVideoCount;

            @Override
            public void open(Configuration parameters) throws Exception {
                creatorVideoCount = getRuntimeContext().getState(
                    new ValueStateDescriptor("creatorVideoCount", Long.class)
                );
            }

            @Override
            public void process(String creatorId, Context context, Iterable elements, Collector<Tuple2> out) throws Exception {
                long currentCount = 0;
                if (creatorVideoCount.value() ! currentCount = 0; if (creatorVideoCount.value() !
                    currentCount = creatorVideoCount.value();
                }

                for (LikeEvent event : elements) {
                    currentCount++; }
                }
                creatorVideoCount.update(currentCount);

                // Output: (creatorId, number of liked videos in 24 hours)
                out.collect(new Tuple2(creatorId, currentCount)); }
            }
        }
    )

    # 6. output the results downstream (e.g. another Kafka topic)
    processed_stream.add_sink(
        KafkaSink.<Tuple2>builder()
        .setBootstrapServers("kafka_broker:9092")
        .setRecordSerializer(
            KafkaRecordSerializationSchema.builder()
            .setTopic("tiktok_creator_video_counts")
            .setValueSerializationSchema(SimpleStringSchema()) # or custom serialization
            .build()
        )
        .build()
    )

    env.execute("TikTok Creator Video Count Job")

# Main function
if __name__ == "__main__".
    env = StreamExecutionEnvironment.getExecutionEnvironment()
    # Configure parallelism as appropriate
    env.setParallelism(4)
    process_tiktok_likes(env)

He ran a few sets of test samples (of course, in real interviews this part is usually conceptual or based on simulated data) and explained, "This scenario leverages Flink's stream processing capabilities to process a huge number of 'like' events in real time. The time complexity can be viewed as O(N), where N is the number of events, since each event is processed only once. With keyBy and window operations, we can efficiently perform aggregation in a distributed environment."

The interviewer then asked, "You just mentioned Flick. in a business like TikTok, which has extremely high latency and throughput requirements, what other technologies or design patterns, besides Flink, do you think could further optimize this statistical process for the extreme peak traffic that might be encountered?"

The interviewer was momentarily stumped; he thought for a few seconds and tried to organize his words, but his delivery was slightly hesitant. We immediately sent an additional prompt in the background, "Consider data pre-aggregation, caching, and options for data storage."

Upon getting the hint, he immediately replied to the interviewer: "In addition to Flink, to further optimize, we can consider the following:"

"1. Pre-aggregation of data (Pre-aggregation): If the sliding interval of our sliding window is acceptable to be slightly larger, we can perform an initial micro-batch aggregation on the data source side or through a lightweight stream processing tool (e.g., Kafka Streams' Processor API) before going to Flink, e.g., aggregating the number of video likes for each creator once per minute, and then sending the results of the aggregation to Flink, which can greatly reduce the number of events Flink needs to process and lower its load."

"2. Distributed Caching: For some very active creators, their video like events may be very frequent. We can use a distributed cache (e.g. a Redis cluster) inside Flink's ProcessWindowFunction to maintain a real-time count of each creator within the current window. This avoids frequent interactions with the state backend and improves processing speed. When the window ends, the final results are then synchronized to persistent storage."

"3. Storage Selection and Index Optimization: The final statistics need to be stored for querying. For this kind of time-series data, we can consider using columnar storage databases (e.g. ClickHouse, Druid) or time-series databases (e.g. InfluxDB), which are well optimized for time-range queries and aggregation. Also, make sure you have the right indexes, such as a compound index on creator_id and publish_time, to speed up queries."

"4. Heterogeneous storage and data tiering: Consider storing the most recent 24 hours of active data in low-latency storage (e.g., Redis or in-memory databases), while historical data is stored in lower-cost but slightly lower query performance storage (e.g., Parquet files on HDFS), creating a hierarchy of data to satisfy different query requirements."

"5. Real-time vs. consistency tradeoffs: At extremely high concurrency, there is sometimes a trade-off between real-time and ultimate consistency. For example, to achieve extreme low latency, we can accept brief statistical inaccuracies that are corrected by subsequent batch processing."

With two timely prompts from us, the interviewer smoothly adjusted his state from confusion, not only gave an efficient solution based on the stream processing framework, but also clearly explained the logic and time complexity of the algorithm, and further gave an optimization solution from the system architecture level, showing good programming skills, system design thinking, and understanding of high-concurrency scenarios. Although the whole process had a little twist and turn, but finally showed the ability to adjust and adapt quickly, and was recognized by the interviewer.