Meta Interview Questions — Page 222

Meta·Data Scientist·Technical Phone Screen

Jul 2026

SQL-heavy technical screen for a Data Scientist role at Meta. Two questions, both pulling from the same video calls table, and the second one was sneakily annoying once you got into the percentage calculation part.

Given a video calls table, how would you find the number of unique callers who have called more than three distinct recipients in the last seven days?
What percentage of customers from France made at least one video call yesterday?

“Pretty standard once you see it.”

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Meta·Software Engineer·Technical Phone Screen

Jul 2026

A data science interview at Meta with a product manager asking a statistics question about user behavior distributions. Pretty short and focused, just the one question from what I remember.

How would you describe the distribution of daily minutes spent on Facebook per user?

“This sounds like a warmup but it really isn't.”

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Meta·Data Scientist·Technical Phone Screen

Jul 2026

A product-heavy Data Scientist case at Meta centered entirely on the parent-teen dynamic on Facebook. Three connected questions that built on each other, which I didn't fully appreciate until I was already in the middle of the second one.

How does parental presence on Facebook affect teen engagement and behavior, and what metrics would you use to measure it?
What combination of data signals would you use to detect parent-child relationships within the social graph?
If parents joining the platform is causing teens to churn, how would you design product interventions to keep both groups engaged without sacrificing one for the other?

“I jumped straight to churn rate and kind of forgot about the softer behavioral signals.”

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Meta·Software Engineer·Technical Phone Screen

Jul 2026

Meta data engineer interview with a coding problem that looked straightforward on the surface but had enough edge cases to keep me busy. One question, heap-based, category grouping with tie-breaking rules.

Given a list of items each with a content_id, category, and rating, write a function that returns the top 3 items per category by rating. Ties should be broken by higher rating first, then lexicographically smaller content_id. The result for each category should be sorted by descending rating, and the solution should aim for O(n log k) per category.

“I got the basic grouping by category pretty fast but the tie-breaking tripped me up for a bit.”

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Meta·Software Engineer·Onsite - Coding / Algorithms

Jul 2026

Meta SWE interview with an AI-enabled coding round built around a classic maze solver problem. Four sub-questions total, mostly debugging, with the last one adding keys and doors as a twist.

Given a maze solver implementation, debug three separate broken versions of the code to identify and fix the issues.
Extend the maze solver to handle keys and doors, where certain paths are only accessible after picking up a specific key.

“Three debugging sub-questions back to back.”

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Meta·Machine Learning Engineer·Onsite - System Design / Architecture

Jul 2026

ML system design round at Meta for an MLE role. The whole thing was a single deep-dive into recommendation systems, and they really wanted you to go end-to-end, not just sketch a two-tower model and call it done.

Design a large-scale recommendation system for a feed or short-video product. Walk through your business objectives and success metrics, how you'd generate candidates, the ranking model and its features, training data and labels, serving architecture and latency constraints, A/B testing strategy, feedback loops, and cold-start handling.

“This is basically the whole interview in one question.”

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Meta·Data Scientist·Technical Phone Screen

Jul 2026

Meta DS interview with a product metrics question about evaluating a new proxy metric for News Feed. Pretty focused session, just the one scenario but it had a few layers to it.

Another team is proposing a new metric as a better proxy for meaningful interactions on News Feed. How would you evaluate whether it's actually worth adopting?

“I started with predictive power which felt right, basically does this metric correlate with outcomes we actually care about.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

Jul 2026

PM interview at Meta with a product sense question about iPhone safety. Pretty open-ended, which I wasn't fully prepared for.

What changes would you propose to make the iPhone the safest smartphone on the market?

“Weird question to get at Meta since it's about an Apple product, but I think that's kind of the point.”

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Meta·Data Scientist·Technical Phone Screen

Jul 2026

SQL-heavy technical screen for a Data Scientist role at Meta. Five tasks back to back, all on the same sales schema, ranging from basic aggregations to Pearson correlation in pure SQL. The complexity ramp was real and I was not ready for the later questions.

Given a deals table and a reps table, write SQL to compute per-rep metrics for Q2 2025: number of closed deals, wins, win rate, average deal amount, and average sales cycle in days. Filter to closed-stage deals with amount > 0, and sort by win rate descending with ties broken by average deal amount.
Deduplicate a touches table by keeping the smallest touch_id per (account_id, touch_date, channel), then define each account's first touch channel in 2025. Join that to 2025 closed deals and produce a report showing first_touch_channel, rep segment, closed deals, wins, and win rate. Include both per-segment rows and a channel-level overall row in a single query.
For each rep, compute the Pearson correlation between prior-week call count and current-week win rate across Q2 2025 weeks. The query should build weekly call counts, build weekly win rates, lag the call count by one week within rep, then aggregate using the closed-form Pearson formula. Exclude reps with fewer than 5 weeks of overlapping data.
Write three separate data quality checks: find deals where closed_at is earlier than created_at, find accounts whose first touch date is after their first deal close date, and find deals whose region does not match their account's region.
As of a fixed reference date, find the top 3 reps by distinct accounts touched via calls in the last 7 days. Apply the same deduplication rule as before. Break ties by total call count, then by lower rep_id.

“This one I actually felt okay about.”

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Meta·Software Engineer·Onsite - System Design / Architecture

Jul 2026

System design round at Meta, one question about building a price tracking service. Pretty open-ended and I spent way too long on the data model before realizing they wanted to talk about scale.

Design a price tracking service similar to CamelCamelCamel, where users can set price drop alerts for products and the system periodically fetches prices from external sources at scale.

“I went deep on the alerting pipeline first, which felt right, but then kind of fumbled when they pushed on how I'd handle millions of tracked products without hammering external sources.”

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