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Meta interview notes, page 227 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 product sense round focused on reimagining the Facebook birthday feature. Three questions, all connected, felt like one long case that kept building on itself.
  • How would you redesign Facebook's birthday experience?
  • What metrics would you use to evaluate success, and how would you prioritize among them?
  • How would you approach launching this redesigned birthday experience?

“I went straight into solutions and had to backtrack when they asked who I was designing for.”

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Meta·Machine Learning Engineer·Technical Phone Screen

Jul 2026
Meta ML Engineer interview with a robot grid exploration problem. No map access, just a move/turn/isTarget API and you have to figure out how to traverse the whole reachable space without looping forever. Classic blind search setup but the unknown initial direction adds a wrinkle most people miss.
  • You control a robot in an unknown 2D grid with walls and open cells. Using only move(), turnLeft(), turnRight(), and isTarget(), implement a function that finds the target cell and stops immediately when reached. The grid boundaries and layout are completely hidden from you.

“The unknown initial direction is the part that trips people up.”

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Meta·Software Engineer·Technical Phone Screen

Jul 2026
Meta SWE coding round with an array manipulation problem. Nothing too wild but the edge cases in the problem statement took me a minute to parse correctly.
  • Given an integer array, find the minimum number of increment-by-1 operations needed to make the array either strictly increasing or strictly decreasing.

“The example they give is [2, 3, 3, 2] with answer 1, which makes sense once you see it, but I initially tried to think about both directions simultaneously and got confused.”

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Meta·Data Scientist·Technical Phone Screen

Jul 2026
Meta DS interview on the ads side. One meaty experiment design question that covered a lot of ground, from traffic splits to stakeholder communication. Felt like a case study more than a typical stats quiz.
  • You've trained a new ad recommendation model and want to know if it should replace the current one. Walk through how you'd design the experiment, what metrics you'd track, how you'd frame results differently for a finance exec versus a growth exec, and what your launch or rollback decision framework looks like.

“This question is broader than it sounds.”

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Meta·Data Scientist·Technical Phone Screen

Jul 2026
Meta DS interview focused on spam detection metrics and experiment design. The questions were more nuanced than I expected, especially around what happens when your primary signal starts moving in the wrong direction.
  • Without access to a spam classifier table, what alternative metrics would you track to monitor both spam activity and overall user experience?
  • If the spam report rate drops, what are the possible explanations and which additional metrics would you look at to figure out what's actually happening?
  • When running an A/B test for an anti-spam intervention where spammers make up a tiny fraction of users, how do you design the test and control groups?

“I jumped straight to report rate and kind of stalled there.”

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Meta·Data Scientist·Technical Phone Screen

Jul 2026
Meta DS interview with a meaty experimentation question about a TikTok-style video feed feature. Just one question but it had a lot of moving parts and I felt like I was still talking when they were ready to move on.
  • Design an A/B test to evaluate a new short-video feed feature. Walk through your choice of experiment unit, randomization strategy, primary and secondary metrics, sample size estimation, guardrail metrics, and how you'd handle novelty effects and seasonality. How would you decide when to call the test?

“This question looks like a standard experiment design prompt until you realize how many layers they actually want you to cover.”

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Meta·Software Engineer·Onsite - System Design / Architecture

Jul 2026
System design round at Meta for a software engineering role. The question was a full leaderboard design problem, which sounds straightforward until you actually have to think through all the edge cases at scale.
  • Design the backend system for an online game leaderboard that supports high write volume, global and friends-based rankings, a player's rank with nearby players, multiple time windows (daily, weekly, all-time), and low-latency reads at scale.

“This one covers a lot of ground and I think I underestimated how many sub-problems are buried inside it.”

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Meta·Data Scientist·Technical Phone Screen

Jul 2026
Meta DS interview with a heavy stats focus. The whole session was basically one long A/B testing problem that kept branching into harder territory. Not a vibe check at all, they wanted to see if you actually know the math.
  • Given an A/B test where both control and treatment have 10 million impressions and CTRs of 1.20% vs 1.23%, calculate the absolute and relative lift, pooled standard error, z-statistic, two-sided p-value, and a 95% confidence interval for the relative lift.
  • If you peeked at the experiment results daily over 14 days without any alpha spending correction, how much does the Type I error inflate, and how would you fix it using either a sequential testing plan with alpha spending or a Bayesian approach with a prior on lift?
  • If you're simultaneously tracking 8 guardrail metrics alongside your primary metric, how do you control for familywise error rate or false discovery rate?

“The arithmetic itself is fine but I fumbled the CI for relative lift.”

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Meta·Data Scientist·Onsite - Behavioral / Leadership

Jul 2026
Meta Data Scientist process, covering everything from scheduling chaos and competing offers to doing real due diligence on a specific team before signing. The questions were more situational and strategic than I expected, less about SQL and more about whether you actually think like someone who's been burned before.
  • Tell me about a time you had to manage interview reschedules, prep for technical rounds, and handle a competing verbal offer all at the same time. How did you communicate with recruiting, set timeline boundaries, and keep relationships intact?
  • You've been team-matched to a specific org but have concerns about stability, layoff risk, and work-life balance. Walk through exactly how you'd vet the team before accepting, including who you'd talk to, what signals you'd look for, and what would make you walk away.
  • Between a verbal offer and a written one, a reorg or headcount freeze happens. What's your escalation plan and how do you decide whether to wait it out or walk?

“This one tripped me up because I started telling a story about logistics and the interviewer clearly wanted something more about judgment calls and communication.”

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Meta·Software Engineer·Onsite - Behavioral / Leadership

Jul 2026
Behavioral round at Meta for a software engineer role. Just one question but it had a lot of parts to it, basically a full self-assessment on the spot.
  • What is a current area where you are actively trying to grow or improve? Walk me through what you are working on, how you figured out it was a gap, what you are doing about it, and whether you have seen any results yet.

“More structured than I expected.”

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