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Meta interview notes, page 69 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·Software Engineer·Technical Phone Screen

May 2026
Meta SWE coding round, one question about weighted random selection using population data. Pretty focused session, no fluff.
  • Given a dictionary mapping city names to their populations, implement a random generator function that returns a city name with probability proportional to its population.

“I knew the general idea (prefix sums plus binary search) but fumbled the implementation details under pressure.”

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Meta·Software Engineer·Technical Phone Screen

May 2026
Interviewed for a data science role at Meta, got a stats fundamentals question that felt deceptively simple but had more to it than I expected.
  • Can you explain Bayes' theorem?

“Thought I had this cold.”

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Meta·Data Scientist·Technical Phone Screen

May 2026
Meta data scientist interview with a meaty offline evaluation question that basically tests whether you know what to do when you can't just say 'run an experiment.' The kind of question that sounds straightforward until you realize how many angles they actually want you to cover.
  • A new recommendation feature is being considered for launch. Using only historical data, how would you assess whether it's worth releasing? Walk through the analyses, metrics, and assumptions you'd rely on. (Note: A/B testing is off the table as an answer.)

“This one tripped me up a bit because my instinct was to reach for an experiment setup and they explicitly blocked that path.”

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Meta·Data Scientist·Technical Phone Screen

May 2026
SQL round at Meta for a Data Scientist role. One question, pretty focused on window functions and ranking logic applied to a search visibility use case. Not a brutal interview but the problem had enough layers to trip you up if you rushed.
  • Given a table of daily shop impressions with columns for date, shop_id, position, and clicks, write a SQL query that computes each shop's average visibility score (defined as 1 divided by position) over the past 7 days, then ranks shops from highest to lowest average visibility and returns shop_id, avg_visibility, and rank.

“I jumped straight to AVG(1.0/position) and almost forgot to filter to the trailing 7 days.”

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Meta·Software Engineer·Onsite - System Design / Architecture

May 2026
Meta EM interview with a system design question about building a price tracker. Pretty open-ended, which I wasn't fully prepared for.
  • Design a price tracking system similar to what Amazon uses to monitor and display product price changes over time.

“I went straight to the data storage layer and spent way too long debating SQL vs NoSQL before even nailing down the core requirements.”

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Meta·Machine Learning Engineer·Technical Phone Screen

May 2026
Meta MLE phone screen, one coding problem the whole time. Pretty focused session, no fluff, just the algorithm.
  • Given a small list of words, find a subset where no letter appears more than once across all chosen words, and the total number of distinct letters covered is maximized. Implement a backtracking solution.

“I knew the LC problem this was based on so I wasn't totally lost, but I fumbled the pruning logic at first.”

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Meta·Data Scientist·Technical Phone Screen

May 2026
Meta DS interview focused entirely on ranking model evaluation for Instagram Shopping. The question was meaty and required knowing both offline metrics and how they connect to actual business outcomes. Not a vibe check round at all.
  • What offline and online metrics would you use to evaluate a product-ranking model for a shopping feed? How do precision and recall apply here, and how do you reconcile offline model metrics with on-site business KPIs like conversion?

“This one took me a second to get traction on.”

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Meta·Software Engineer·Onsite - System Design / Architecture

May 2026
Meta Research Scientist onsite with an ML system design question on detecting weapon sales. Pretty open-ended, which I wasn't fully prepared for.
  • Design an ML system to detect weapon sales on a social platform.

“This is the kind of question where you think you know where to start and then realize halfway through that you've already gone down a rabbit hole.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

May 2026
Got a product sense question at Meta about ad placement timing on a streaming platform. Pretty open-ended, which I wasn't fully prepared for.
  • How would you predict the optimal time to show commercials on a streaming platform like Hulu?

“I went straight to user engagement signals, watch time patterns, drop-off rates around natural content breaks.”

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Meta·Data Scientist·Technical Phone Screen

May 2026
SQL-heavy technical screen for a Data Scientist role at Meta. Three sub-parts all built on the same two-table schema, escalating from aggregation logic to percentage calculations to index design. Felt more like a take-home graded in real time than a conversation.
  • Given an interactions table and a products table, write SQL to return the count of products where the number of distinct buyers across all history is greater than 3 AND the total interaction count summed across all rows is greater than 10. Explain why you use HAVING instead of WHERE for these thresholds.
  • For US products only, compute the percentage of 'validate' interactions (by interaction_count) out of all interactions in the 7-day window ending on 2025-09-01. Use an INNER JOIN to filter to US products, explain why INNER JOIN is better than LEFT JOIN here, specify how you handle a zero denominator, and describe your rounding approach.
  • Write the complete SQL for both parts above, then specify which indexes you would add to each table to make these queries efficient. Also explain conceptually why HAVING is required for post-aggregation filters and WHERE cannot be used in that context.

“The DISTINCT buyer part is where people slip up.”

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