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Meta interview notes, page 41 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

Apr 2026
Meta product sense round, one question about building a sports feature into Facebook. Pretty open-ended, which sounds fun until you're actually sitting there trying to figure out where to even start.
  • How would you design a new sports feature for the Facebook app?

“I went straight to live scores and match updates because that felt obvious, but I realized pretty quickly I was just describing ESPN inside Facebook.”

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Meta·Software Engineer·Hiring Manager Screen

Apr 2026
Interviewed at Meta for a SWE role, and the whole thing kicked off with the most standard opener you can imagine.
  • Walk me through what you're currently working on in your role.

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Meta·Data Scientist·Technical Phone Screen

Apr 2026
A product-heavy DS interview at Meta, this one was essentially a full case study on whether you'd ship a feature to Instagram based on Facebook data. Dense, multi-part, and the kind of question where you can feel yourself running out of runway halfway through.
  • A 'More like this' button that surfaces similar products exists on Facebook but hasn't launched on Instagram. You have access to interactions and product schemas but no button telemetry on Instagram and can't use revenue as your goal. How would you build pre-launch evidence using existing data to convince a PM this feature is worth building?
  • How would you design the experiment for this feature launch, and specifically how would you choose the randomization unit given potential network effects between users?
  • What would your primary and secondary metrics be for this experiment, keeping in mind engagement is the goal and not revenue? What guardrails would you set and what event logging would you add?
  • Walk through how you'd compute sample size and minimum detectable effect under cluster randomization. What assumptions do you need and how would you handle multiple looks at the data during the experiment?
  • How would you define launch criteria combining statistical and practical significance, and how would you stage the rollout if results are promising in some segments but not others?

“I tried to anchor on exploration rate as my leading indicator, basically sessions where a user touched at least one similar-product interaction divided by total sessions.”

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Meta·Software Engineer·Technical Phone Screen

Apr 2026
Did a technical screen at Meta centered on validating Bitcoin transactions. Not a ton of context to go on, but it was clearly a coding-focused session.
  • Given a set of Bitcoin transactions, write a function to validate them according to standard transaction rules.

“This is the kind of problem that looks straightforward until you start thinking about what 'valid' actually means in a blockchain context.”

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Meta·Software Engineer·Onsite - Coding / Algorithms

Apr 2026
Meta SWE coding round with a binary search problem. Pretty standard algorithmic question but the O(log n) constraint is what makes it interesting, easy to miss if you just go for the linear scan first.
  • Given an integer array, find and return the index of any peak element, where a peak is strictly greater than its neighbors. Your solution must run in O(log n) time.

“My first instinct was a linear scan, which obviously works but completely ignores the time constraint.”

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Meta·Product Manager·Onsite - Behavioral / Leadership

Apr 2026
Behavioral round at Meta, two big questions and a lot of time to hang yourself with your own answers if you're not careful. The failure question in particular felt like it had some teeth to it.
  • Tell me about a project where you had to collaborate closely with other teams. What was your role, what challenges came up, and what did you actually do to move things forward?
  • Walk me through a project that failed or didn't hit its goals. What happened, what did you do, and what would you do differently now?

“I had a decent story ready for this but I kept second-guessing whether my 'role' was senior enough sounding.”

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Meta·Software Engineer·Technical Phone Screen

Apr 2026
Meta coding interview, just one problem about finding the longest repeating substring. Not much context given but it's the kind of question that sounds straightforward until you're actually in it.
  • Find the longest repeating substring in a given string.

“This one is sneakier than it looks.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

Apr 2026
Meta product sense question, just the one about search. Not much context to go off of but it's a classic.
  • How would you improve Facebook Search?

“This is the kind of question where you think you know where to start and then realize halfway through that you've been talking about the wrong user segment for two minutes.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

Apr 2026
Product sense interview for a PM role, centered entirely on designing a parking feature inside Google Maps. The question was structured but sprawling, covering user segmentation, MVP scoping, metrics, and monetization all in one go. Felt like a mini case study more than a typical interview question.
  • How would you design a parking-finding experience within Google Maps to help urban drivers reduce uncertainty and wasted time when parking near a destination?
  • Who is your target user segment for this parking feature, and why not try to solve for every driver everywhere from day one?
  • What would your MVP look like, and which features would you defer and why?
  • How would you define success metrics for this feature, and what counter-metrics would you track to catch harmful outcomes?
  • What monetization options would you consider, and how do you prevent paid placements from undermining user trust?
  • How would your approach differ for street parking versus paid garages, and what would you do if your availability predictions were only 60% accurate?
  • What local regulations or safety concerns would you investigate before launching this in a new city?
  • How would you pilot this feature in one city before scaling, and what would you need to see before expanding?

“This one is massive.”

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Meta·Machine Learning Engineer·Onsite - Behavioral / Leadership

Apr 2026
Behavioral round for an ML Engineer role at Meta. Just one question but they really sat in it with me for a while, lots of follow-ups about communication and trade-offs specifically.
  • Tell me about a time you had to produce high-quality work under a tight deadline while juggling multiple tasks at once. How did you decide what to prioritize, communicate trade-offs to others, and stay composed? What happened, and what would you change?

“The part that tripped me up was the 'stay calm' angle.”

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