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Meta interview notes, page 212 of 238

FAANG / Big Tech

Most of these Meta write-ups come from Software Engineers (1,194), then Data Scientists (489) and Product Managers (453). The most reported rounds are the technical phone screen (1,190), the product sense round (466), the system design round (255) and the behavioral / leadership round (205).

2377 interview notes · updated Jul 2026

Meta·Data Scientist·Technical Phone Screen

Jun 2026
Meta data scientist interview that went deep into statistics for a social platform feature. The whole session was basically applied stats under pressure, and I left feeling like I'd passed a grad school exam I hadn't studied for.
  • How would you compute the expected value of the number of comments per post on a social platform feature?
  • State the Central Limit Theorem and explain why it matters in the context of analyzing comment counts per post.
  • When should you use population standard deviation versus sample standard deviation when working with comment count data?
  • Build a 95% confidence interval for the mean number of comments per post and walk through what it actually means.

“Seemed straightforward at first.”

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Meta·Software Engineer·Onsite - System Design / Architecture

Jun 2026
System design round at Meta for a software engineering role. One question, but it had a lot of surface area and I felt like I kept getting pulled in different directions trying to cover everything.
  • Design a ticket-booking system where a reserved ticket automatically returns to the available pool if payment isn't completed within 30 minutes. Walk through the data model, reservation flow, concurrency handling, timeout mechanism, and consistency guarantees.

“This question has way more moving parts than it looks.”

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Meta·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026
Went through a behavioral round at Meta and got hit with the classic failure question. Not much else to report.
  • Tell me about a time you failed at work.

“I had a story ready but it felt too small in the room.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Meta PM interview, one question about diagnosing a metric drop for Facebook Login. Pretty focused case, no behavioral fluff, just straight into the product analytics problem.
  • Successful logins via Facebook's third-party authentication product are declining on a Monday. How do you investigate and respond?

“The Monday detail is doing a lot of work here and I almost glossed over it.”

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Meta·Data Scientist·Technical Phone Screen

Jun 2026
Meta data scientist interview that leaned heavily into stats fundamentals around count data and hypothesis testing. One question but it had a lot of moving parts and the follow-ups came fast.
  • Given the full distribution of comment counts per article on a content platform, what summary statistics would you report and why? When does the mean become a bad representation of the data compared to the median? How would you identify and handle outliers in this kind of count data? And if a product team wants to know whether a UI change actually moved engagement, what statistical test would you use?

“This felt like four questions duct-taped together and I kind of answered them in the wrong order.”

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Meta·Software Engineer·Technical Phone Screen

Jun 2026
Meta SWE coding round, one algorithm question the whole time. Pretty standard selection problem but they wanted you to talk through complexity out loud which I wasn't fully prepared for.
  • Given an integer array and a value k, return the k-th largest element in the array. You also need to state the time complexity of your approach.

“I knew this problem.”

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Meta·Software Engineer·Onsite - Coding / Algorithms

Jun 2026
Meta SWE coding round, one algorithmic question on finding a local minimum in an array. The constraint was O(log n) so binary search was the expected approach.
  • Given an integer array, find and return the index of any local minimum, where the element is strictly smaller than its neighbors. Boundary elements only need to be smaller than their single neighbor. The solution must run in O(log n) time.

“My first instinct was a linear scan and I actually started explaining it before catching myself.”

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Meta·Machine Learning Engineer·Onsite - System Design / Architecture

Jun 2026
Brutal system design round at Meta for an MLE role. One massive question that basically asked me to design an entire MoE pretraining stack from scratch, covering architecture, parallelism, fault tolerance, and scaling. Felt like they wanted a staff-level answer in 45 minutes.
  • Design a large-scale Mixture-of-Experts pretraining pipeline for a bilingual LLM trained on roughly 1 trillion tokens across 256 A100 GPUs. Cover model architecture, parallelism strategy, communication patterns, load balancing, memory and optimizer sharding, checkpointing, dataset curation, tokenization, training schedule, and monitoring. Also discuss common failure modes and how you'd scale the system to 1,024 GPUs.

“This was basically the whole interview.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Meta product sense round, one question about healthcare navigation. Pretty open-ended, which I wasn't fully prepared for.
  • Design a product that helps patients find the right medical care.

“I jumped straight into solutions and had to backtrack once I realized I hadn't defined who the patient was or what 'right care' even means.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
PM product design question for Meta's Social Good team. One question, open-ended, the kind where you can go a hundred directions and that's exactly the problem.
  • You're the PM for Meta's Social Good team. Design a product that encourages more people to volunteer.

“I spent the first few minutes trying to define the problem space and nearly talked myself into a corner debating whether to focus on discovery (finding opportunities) or commitment (following through).”

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