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Meta interview notes, page 182 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·Machine Learning Engineer·Onsite - Behavioral / Leadership

Jun 2026
Meta MLE behavioral round, basically the whole 45 minutes spent dissecting one project from every possible angle. Thought it would be a warmup question but nope, they just kept going deeper.
  • Tell me about a project you are most proud of.
  • How did you plan and break down the work for that project?
  • When scope or timeline got tight, how did you decide what to cut or keep?
  • How did you work with cross-functional partners like PMs, designers, data teams, or infra during the project?
  • Were there any disagreements or conflicts during the project, and how did you handle them?
  • What was your specific contribution versus what the team did collectively?
  • What was the measurable outcome of the project, and what would you do differently?

“This is the whole interview, not just an opener.”

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Meta·Data Scientist·Technical Phone Screen

Jun 2026
Meta data scientist interview with a statistics and product analytics focus. Three questions covering descriptive stats, probability, and metric divergence diagnosis. The probability one was straightforward but the last question is where things got interesting and a bit uncomfortable.
  • Given a distribution of video view counts, how would you compute and interpret the mode, median, and mean? What relationship do you expect between these three for this type of data, and which would you actually report?
  • If each video a user watches has a 10% chance of being shared independently, what's the probability that at least one out of ten videos gets shared?
  • You notice that average watch time per video is going up while average share rate per video is going down at the same time. What could explain this divergence and how would you test your hypotheses, separating causal from correlational explanations?

“This felt like a warmup but I almost tripped on the 'which should you report' part.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Meta PM interview, product design question about building something for the contractor-finding space. Pretty open-ended, which I wasn't fully prepared for.
  • Design a product that helps people find contractors.

“I jumped straight into solutions, which was the wrong move.”

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Meta·Product Manager·Onsite - Behavioral / Leadership

Jun 2026
Interviewed at Meta for what seemed like a product or program management role. One behavioral question, pretty standard stuff, but it hit different than I expected.
  • Tell me about a recent project where you identified areas for improvement or experienced failure. How did you recognize the issues, and what did you do about them?

“I went in thinking I'd tell a clean story about a project that went sideways and how I fixed it.”

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Meta·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Meta software engineer interview with a system design question around building a trade logging class. Pretty focused on data structure choices and sort behavior, which I wasn't fully prepared to defend under pressure.
  • Design a class that records a user's daily stock trades and retrieves them in sorted order. The class needs a method to log a trade (symbol, side, price, quantity, timestamp) and another to return all trades sorted by time first, then by total trade value. How do you structure this so inserts stay efficient and you're not re-sorting on every retrieval?

“I jumped straight to 'just use a list and sort on read' and the interviewer immediately pushed back.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Meta PM interview, one question about picking a True North metric for Facebook Stories. Short and focused, but not as easy as it sounds when you're actually in the seat.
  • What metrics would you choose as the True North metric for Facebook Stories?

“I went straight to engagement and kind of talked myself in circles.”

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Meta·Machine Learning Engineer·Onsite - Behavioral / Leadership

Jun 2026
Behavioral round for an MLE role at Meta. Pretty standard stuff, nothing that should've surprised me, but I still fumbled a couple of them by over-explaining instead of just getting to the point.
  • Tell me about yourself.
  • Describe a time you had a conflict with a teammate. How did you handle it?
  • Tell me about a time you failed. What did you take away from it?
  • Walk me through a project you led from start to finish.
  • What's the most impactful contribution you've made in your career?

“I rambled.”

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Meta·Software Engineer·Technical Phone Screen

Jun 2026
SQL question from a Meta SWE interview, filtering categories by author diversity then ranking customers by purchase volume. Pretty clean problem once you break it into steps, though the nested aggregation part is where things get tricky.
  • Given a Purchases table (CustomerId, BookId, Category) and a Books table (BookId, Author), find the top 3 customers by number of books purchased, but only count purchases from categories that have books by at least 3 distinct authors.

“The filtering step tripped me up a bit.”

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Meta·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026
Meta software engineer behavioral round. They covered pretty much every BQ topic imaginable, and two questions were ones I hadn't seen in the standard prep material, both touching on AI.
  • Tell me about a time you used AI or machine learning to drive meaningful impact on a product or system.
  • How do you stay current with developments in AI, and how have you applied something you recently learned to your work?

“This one tripped me up a bit because my examples felt thin.”

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Meta·Data Scientist·Technical Phone Screen

Jun 2026
Interviewed at Meta for a data-related role and got a question around handling missing data. Not much else to go on from what I remember.
  • How would you handle missing data in a dataset?

“Pretty standard but I still fumbled the structure a bit.”

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