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Meta interview notes, page 221 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·Data Scientist·Technical Phone Screen

Jul 2026
Meta DS interview focused entirely on designing an ad ranking system end-to-end. It was a deep technical session covering modeling, evaluation, and all the messy real-world constraints that come with running ads at scale. Dense question, lots of ground to cover.
  • How would you define the objectives for an ad recommendation system in a content feed, and how would you balance revenue against user experience?
  • What modeling approach would you use to predict CTR, CVR, and revenue for ads, and how would you handle delayed conversion feedback?
  • How would you address selection bias when evaluating a new ranking policy if your training data came from a previous policy's logged decisions?
  • How would you approach exploration vs. exploitation in an ad ranking system?
  • How would you handle cold start for new ads and new users in your ranking system?
  • How would you incorporate advertiser budget pacing constraints into the ranking and serving system?
  • Walk me through your offline and online evaluation plan for this ad ranking system, including what guardrail metrics you would set.

“I started with the obvious revenue angle and then pivoted to user experience, but I fumbled the part about how to actually combine them.”

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Meta·Software Engineer·Onsite - Behavioral / Leadership

Jul 2026
Interviewed for an EM role at Meta, pretty standard behavioral round focused on leadership. Just the one question I can remember clearly.
  • Tell me about a time you led a cross-functional team.

“Classic question but I still fumbled the structure a bit.”

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Meta·Software Engineer·Onsite - System Design / Architecture

Jul 2026
Meta MLE onsite covering recommendation system design. The focus was on label quality issues and model selection trade-offs, with a lot of open-ended back-and-forth rather than clean textbook answers.
  • How would you design a recommendation system, and how do you handle label quality issues that arise during training?
  • When would you choose a gradient boosting model like XGBoost over a neural approach in a recommendation setting, and what are the downsides?

“This is where most of the time went.”

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Meta·Data Scientist·Technical Phone Screen

Jul 2026
Meta DS interview with a meaty product analytics case that covered basically everything: metric design, root cause analysis, experiment design, and quasi-experimental fallbacks. One long question, but it had a lot of layers and the interviewer expected you to drive the structure yourself.
  • A new feature launches or a key metric suddenly shifts on a consumer app. Walk through how you'd diagnose what changed and why, size the impact, define north-star and guardrail metrics, map hypotheses onto the user funnel, and design an experiment to validate your findings. If an A/B test isn't feasible, what's your alternative?

“This is one of those questions that sounds like five questions stitched together, because it is.”

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Meta·Data Scientist·Technical Phone Screen

Jul 2026
Meta data scientist interview with a probability question that felt deceptively simple on the surface. Conditional expectation with a non-empty room constraint, the kind of thing that trips you up if you just pattern-match to the unconditional case.
  • k meetings are randomly assigned across N rooms. Given that Room 1 already has at least one meeting, what is the expected number of meetings in Room 1?

“My first instinct was just k/N and I almost said it out loud.”

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Meta·Product Manager·Hiring Manager Screen

Jul 2026
Interviewed at Meta for what seemed like a PM or engineering lead role. Just one question worth noting, and it was a methodology judgment call that I fumbled more than I'd like to admit.
  • How would you decide whether Agile methodology is the right fit for a given project?

“I went straight into listing Agile benefits and kind of missed the point.”

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Meta·Software Engineer·Technical Phone Screen

Jul 2026
Meta coding screen, one question the whole time. Pretty focused on expression parsing which I don't practice enough. Left feeling like I got through it but not cleanly.
  • Write a function that evaluates a math expression given as a string. The expression contains non-negative integers, plus, minus, multiply, and divide operators, and spaces. No parentheses. Division truncates toward zero. No built-in eval allowed. Must handle long inputs efficiently, and you need to explain time and space complexity.

“My first instinct was to just scan left to right and apply ops as I go, which breaks immediately once you account for operator precedence.”

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Meta·Product Designer·Onsite - Product Sense / Strategy

Jul 2026
Meta product design interview, one question about improving the Oculus homepage. Not much context shared beyond the question itself.
  • How would you improve the Oculus homepage?

“Classic product sense question but it has more surface area than it looks.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

Jul 2026
Meta PM interview with a single product design question about building a job search experience on Facebook. Pretty open-ended, which sounds freeing until you're actually sitting there trying to figure out where to start.
  • How would you design a job search product for Facebook?

“I spent probably too long on the user segmentation piece and then rushed the actual feature design at the end.”

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Meta·Machine Learning Engineer·Technical Phone Screen

Jul 2026
Meta ML engineer interview, one coding/ML question about implementing k-means from scratch. Pretty barebones experience to report but it was a real one.
  • Implement k-means clustering from scratch.

“I knew the algorithm well enough but fumbled on the initialization part.”

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