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Meta interview notes, page 28 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·Software Engineer·Onsite - System Design / Architecture

Apr 2026
System design round at Meta for a software engineer role. The question was a full Netflix-style platform design, which sounds like a classic but the scope they expected you to cover was genuinely a lot to fit into one session.
  • Design a Netflix-like video streaming platform, covering the full pipeline from upload and transcoding through CDN delivery, adaptive bitrate streaming, metadata, user profiles, recommendations, search, and DRM.

“This question is basically a gauntlet.”

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Meta·Data Scientist·Technical Phone Screen

Apr 2026
Analytical execution round for a Data Scientist role at Meta's ads team, heavy on probability theory and statistics. The whole thing felt like a grad school exam more than a job interview, which I was not fully prepared for.
  • Name three probability distributions suitable for modeling ad clicks, state the core assumption behind each, and write out the expectation and variance formulas for all three.
  • If 90% of users are high-intent and 10% are low-intent, and both groups have an expected click-through rate of 0.30, what is the overall expected CTR?
  • Sketch the PDF of an exponential distribution and explain what happens to its shape as sample size grows very large.

“I went with Bernoulli, Binomial, and Poisson, which felt right.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

Apr 2026
Meta PM interview, product sense round. Just one question but it took up the whole session and I left feeling like I probably underthought it.
  • If you had to build a Netflix dashboard using only 3 metrics, which ones would you choose?

“I jumped straight to engagement metrics and said something like watch time, retention rate, and content completion rate.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

Apr 2026
Meta product sense interview, one question about metrics for a photo sharing feature. Not a lot of context to go on but it's a pretty classic Meta PM question.
  • What metrics would you prioritize to measure the success of a photo sharing feature on Facebook?

“The tricky part isn't listing metrics, it's knowing which ones actually matter and why.”

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Meta·Software Engineer·Technical Phone Screen

Apr 2026
Meta AI Research Scientist interview where the main event was a deep technical dive into a project from your resume. They really do mean deep, every decision you made is fair game.
  • Walk me through a recent AI/ML research project in depth: the problem, the data, the design choices you considered, the trade-offs, what failed and how you debugged it, the final results, and what you'd do differently.

“This is not a 'tell me about your work' warmup.”

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Meta·Software Engineer·Technical Phone Screen

Apr 2026
Meta SWE interview with a tree traversal problem. Pretty standard coding round, nothing too wild, but the edge cases in this one can sneak up on you if you're not careful.
  • Given a binary tree, return its vertical order traversal column by column, top to bottom. If two nodes share the same row and column, list them left to right.

“I knew BFS was the right move but fumbled on how to track column indices cleanly.”

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Meta·Software Engineer·Technical Phone Screen

Apr 2026
Got a classic product analytics case for a data science role at Meta. Just the one question but they clearly wanted to see a structured diagnostic approach, not just a list of things to check.
  • You're a data scientist at Meta and Facebook's daily post views suddenly drop 10%. How do you investigate?

“I started with data integrity because I've been burned before by jumping to product explanations when it was actually a logging bug.”

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Meta·Data Scientist·Technical Phone Screen

Apr 2026
Meta data scientist SQL round, two tasks in one prompt. The schema setup was clean but the edge cases around missing hashtag IDs and deduplication were where things got interesting fast.
  • Given a table of hashtag follow events and a hashtag reference table, deduplicate exact duplicate rows and then rank each traffic source by total follows today. Return the source name, follow count, and rank, breaking ties alphabetically.
  • Using the same deduplicated data, calculate what percentage of today's follows from the hashtag page source are on hashtags flagged as violating. Return a single value rounded to two decimal places.

“The dedup part is what gets you.”

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Meta·Data Scientist·Technical Phone Screen

Apr 2026
Product analytics case for Meta's DS role, focused on a calling product. The whole thing was one big open-ended question about group calling, and they really wanted you to go deep on experimental design and proxy metrics. Felt more like a strategy session than a typical SQL interview.
  • You only have one-to-one call log data and a DAU table. How would you figure out whether there's real unmet demand for a group calling feature before it even exists?
  • If you launched group calling, how would you define and measure whether the launch was actually successful, both short-term and long-term?

“This is the kind of question where the first instinct is to say 'look at call volume' and then realize that tells you basically nothing about group demand.”

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Meta·Software Engineer·Onsite - System Design / Architecture

Apr 2026
Meta data engineer interview with a system design question about schema design for a ride sharing app. Pretty open-ended, which I wasn't fully prepared for.
  • Design a database schema for a ride sharing application.

“I started with users and rides tables and felt okay for the first few minutes, but then they pushed on things like driver availability, pricing history, and how you'd handle trip states over time.”

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