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Meta interview notes, page 219 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·Product Manager·Onsite - Product Sense / Strategy

Jul 2026
Meta PM interview with a product design question about getting people back into exercise habits. Pretty open-ended, which I both appreciated and struggled with.
  • Design a product to help people restart an exercise habit.

“I spent too long on the problem space and not enough time actually proposing something concrete.”

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Meta·Software Engineer·Onsite - Coding / Algorithms

Jul 2026
Meta SWE coding round built around a friend recommendation system, three sub-tasks that escalated from a straightforward bug fix to a graph-scoring problem with a complexity discussion at the end. The AI-assisted angle was a bit unusual but the underlying CS was pretty standard graph traversal stuff.
  • There's a bug in the valid_recommend function of a User class. Find and fix it.
  • Implement a random_recommend function that picks a random non-friend user for a given user.
  • Implement a Top-K friend recommendation function that ranks candidates by number of mutual friends. Then walk through the time complexity and discuss how the scoring metric would change for second-degree connections.

“The bug was pretty subtle if you didn't read the starter code carefully.”

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Meta·Machine Learning Engineer·Onsite - Behavioral / Leadership

Jul 2026
Behavioral loop for an MLE role at Meta. Seven questions, all the usual introspective stuff. Nothing technically deep but a few of them made me squirm more than I expected.
  • Which project are you most proud of, and why?
  • What was the hardest challenge you faced in that project?
  • How did you handle a situation where someone pushed back on your approach or disagreed with you?
  • Tell me about someone who was difficult to work with and how you navigated that.
  • Walk me through a past failure and what you took from it.
  • What constructive feedback have you received from managers or peers?
  • What skills or habits are you actively working to improve going forward?

“I had a go-to answer for this but partway through I started second-guessing whether it was impressive enough for Meta's scale.”

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Meta·Machine Learning Engineer·Technical Phone Screen

Jul 2026
Meta MLE interview that went deep into sparse linear algebra, which I honestly did not expect to spend so much time on. The AI-assisted coding angle was a nice twist but also meant the bar for understanding the tradeoffs was higher, not lower.
  • Implement a dot product between two sparse vectors and a sparse matrix multiplication routine.
  • Compare COO, CSR, and CSC sparse storage formats. For each of these operations, which format is most appropriate and why: computing a sparse vector dot product, left-multiplying a dense vector by a sparse matrix, and multiplying two sparse matrices together?
  • What are the time and memory complexity of these sparse operations in terms of nnz (number of non-zeros)?

“I went straight to COO for the dot product because it felt natural, two lists of (index, value) pairs and you merge them.”

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Meta·Data Scientist·Technical Phone Screen

Jul 2026
Tough technical screen at Meta for a data scientist role, heavy on sequential testing theory. The questions were genuinely hard and felt more like a stats PhD oral exam than a typical DS interview.
  • Compare Pocock and O'Brien-Fleming alpha-spending functions for an experiment with two interim looks. What are the corresponding critical z-values under each approach?
  • How do mixture-SPRT or e-values allow valid inference no matter how many times you peek at the data?
  • If you are running sequential monitoring across multiple metrics simultaneously, how do you keep overall error rates under control?

“This is where I started to sweat.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

Jul 2026
Interviewed at Meta for a product role and got hit with the classic newsfeed ads volume question. Short session, not a lot of context given upfront.
  • How many ads should Facebook's Newsfeed show to users?

“This is the kind of question where you think you know what they want and then halfway through your answer you realize you've been talking about the wrong thing.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

Jul 2026
Meta PM interview with a product design question focused on college students. Pretty open-ended and I kept second-guessing whether I was going broad enough or too broad.
  • Design a product that helps college students connect with each other.

“My first instinct was to jump straight into features, which was probably the wrong move.”

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Meta·Software Engineer·Technical Phone Screen

Jul 2026
Meta coding screen, pretty much a classic interval problem. Nothing fancy about the setup but it's the kind of question where you either see the pattern immediately or you spend five minutes spinning your wheels.
  • Given an array of intervals, merge all overlapping ones and return the resulting non-overlapping intervals.

“Sort by start time first, that's the whole key.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

Jul 2026
PM interview at Meta, single product metrics question about the notification bell feature. Pretty focused session, no behavioral fluff, just straight into the case.
  • You're a PM at Meta. How would you measure the success of Facebook's notification bell and its drop-down menu?

“I jumped straight to engagement metrics and kind of forgot to set up the goal first.”

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Meta·Data Scientist·Technical Phone Screen

Jul 2026
Meta data scientist interview focused on statistical reasoning around a short-video platform. Two questions, both surprisingly conceptual rather than pure SQL or coding. Felt more like a product analytics case study than a traditional DS screen.
  • You're given view counts across videos on a short-video platform. Describe or sketch the distribution, and report the mode, median, mean, and 99th percentile.
  • Two users on the platform consistently have a lot of the same videos in their top-10 recommendation lists. Is that a good sign or a problem, and how would you evaluate it statistically?

“I knew this was going somewhere Zipf-like but I fumbled the actual description at first, started talking about a normal distribution before catching myself.”

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