I just finished the interview with Meta. In summary, it was smoother than I expected, but I can also clearly feel that Meta does not rely on brushing people in a certain round, but slowly widens the gap through "combination punches". Coding itself is not abnormal, but these two rounds of AI Coding + System Design will really determine whether you can go far. While the memory is still fresh, let me briefly share the complete journey from OA to Onsite Meta Interview Process And some real body sensations.
Meta OA process
Meta's OA is more like an "entry ticket", not a place that determines victory or defeat.
- Question type: Two algorithm questions + one small system design/logic question
- Examples: array/string processing, DFS/BFS or basic sorting + simple HashMap
- Time: Approximately 30-45 minutes per question
- Somatosensory: Moderate difficulty, focusing on your correct and clear writing
- Test Preparation Strategies:
- Regular LeetCode Easy/Medium question types are enough
- Pay attention to clean code and reasonable naming
- Familiarity with the interview platform environment is also important (Python/Java/C++ are all acceptable)
OA itself will not widen the gap between the crowd, but if it is not done well, it will die directly, so just play it slowly and steadily.
Onsite overall experience
Onsite has four rounds in total. The overall pace is easier than expected, but each round has obvious focus. Let’s talk about Behavioral first. The interviewer opened my resume and asked me what was the biggest challenge I encountered in a certain project. I replied that the amount of data had increased dramatically at that time, and the original pipeline could not run at all. I proposed a batch processing + parallel computing solution. The interviewer asked if I had tried other solutions. I explained that I had optimized it on a single machine, but the bottleneck was still IO. The problem could be solved by running it in batches in parallel. The interviewer nodded and smiled to show understanding. After the entire BQ round, I think the key point is to clearly explain your thoughts and reasons for decision-making, rather than memorizing the STAR template.
Coding (above average)
The questions in the Coding round were moderate. I encountered a two-dimensional matrix to find the square area with the largest continuous 1, and a simple DFS/BFS question. I first drew an idea diagram on paper and told the interviewer that I wanted to use dp[i][j] to represent the side length of the largest square with (i,j) as the lower right corner. The interviewer asked how to deal with the boundary conditions. I said that if i=0 or j=0, it is directly equal to matrix[i][j]. Otherwise, take the minimum value of the upper, left, and upper left + 1. The interviewer asked me to write out the complete code. I verbalized it while writing, checked the corner case, and finally added a print verification example. I feel that this round is not about cunning skills, but about clear thinking, neat code, and complete corner cases.
System Design (entry level, but critical)
System Design is entry-level, but it’s not something to be taken lightly. I drew an architecture diagram explaining the responsibilities of each module. The interviewer asked what to do if the number of users doubled the database. I said that it could be divided into shards + add a cache layer, and asynchronous writing would reduce the pressure. The interviewer asked again what to do if the shard goes down, and I explained the failover + replication mechanism. After the whole round, I found that Meta mainly tests your engineering thinking and expanded awareness. There is no need to show off your skills, but it must be clearly stated why each module is designed in this way.
AI Coding (the round that widens the gap)
Finally, there is AI Coding and it is definitely the round that widens the gap. The language I often use is no longer in the supported list, so I can only switch to Python. After the interviewer confirmed that I could use Python, I read the question and verbalized the idea: "First tokenize, then use Counter to count the frequency of keywords, and finally return to the top N." The interviewer asked how to deal with a large amount of text, and I said that I could use a generator to process it in blocks, reduce memory usage, and process the text in parallel. The interviewer's eyes lit up: "Very good, you have considered scalability." This round is obviously not a test of grammar, but a test of quick understanding of problems, implementation of code and scalability.
Overall experience & preparation points
- Strength: Above average, but feels fair
- Key points for exam preparation:
- AI Coding (the most critical)
- System Design
- Coding foundation is stable
- BQ never loses the chain
Meta depends more on whether you have engineering thinking and adaptability, rather than relying on luck to solve the questions.
Before the interview, you must not only answer questions, but also practice your thinking + engineering thinking + communication skills.
Don’t let AI Coding ruin your offer
Meta's recruiting window is short and Headcount is fleeting. You may have refreshed 500 LeetCode, but if you get stuck in the language limitations of AI Coding or the communication of System Design, all your previous efforts will be lost.
ProgramHelp Core values:
- Ex-FAANG Mentor Group: We are not just “question experts”, we have technical experts from Stanford/CMU who are proficient in the Python/ML technology stack and perfectly cover the AI Coding round.
- Full process real-time assistance (Live Support): From OA to VO, we offer screen sharing + live voice coaching. You just type, we provide the ideas and code.
- Zero risk control: We understand the logic of risk control and teach you how to "act out" the problem-solving process naturally, rejecting mechanical copying.
Investment advice: Instead of spending a few months worrying about "blind people feeling the elephant", it is better to lock in an offer with an annual salary of $180k+ without investing 10% of the first month's salary.
