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Meta interview notes, page 42 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·Onsite - Behavioral / Leadership

Apr 2026
Meta onsite behavioral loop for a Data Scientist role, focused almost entirely on cross-functional influence, conflict, and self-awareness. No technical questions at all, which I wasn't fully expecting going in.
  • Tell me about a time you influenced stakeholders without having formal authority over them. What happened and what was the result?
  • Describe a significant conflict or disagreement you had with someone on your team and how you worked through it.
  • When would you choose to escalate a problem early rather than trying to resolve it yourself first?
  • What is your biggest professional weakness, and what are you actively doing to get better at it?

“This one I felt okay about.”

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Meta·Software Engineer·Technical Phone Screen

Apr 2026
Meta coding screen, one question about arrays. Short session, nothing else to report.
  • Given an array of integers, determine whether the elements form a sequence of consecutive integers.

“Seems easy until you start thinking about edge cases.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

Apr 2026
Meta product sense round, one question about forecasting user behavior after a product change. Pretty sparse on details but the question itself has some meat to it.
  • How would you predict how users will behave after a significant product update?

“This one tripped me up more than I expected.”

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Meta·Software Engineer·Onsite - System Design / Architecture

Apr 2026
Meta system design round focused entirely on building a search system for posts, basically a stripped-down Elasticsearch from scratch. Pretty intense scope for a single session, lots of follow-ups on sharding strategy and ranking.
  • Design a system to search Facebook posts: given keyword queries, return relevance-ranked results at scale.
  • How would you handle real-time post ingestion into the search index without taking down read performance?
  • Walk through your ranking approach for search results. How would you go beyond simple keyword matching?

“This one is deceptively wide.”

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Meta·Machine Learning Engineer·Technical Phone Screen

Apr 2026
Meta MLE interview with a coding question on string manipulation. Pretty standard algorithmic problem but the edge cases are where it gets tricky.
  • Given a string containing parentheses and lowercase letters, remove the minimum number of parentheses to make the string valid. Return any valid result.

“I went straight to a stack-based approach and it worked, but I fumbled explaining the minimum removal part.”

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Meta·Data Scientist·Technical Phone Screen

Apr 2026
Meta data scientist SQL round, two-part question on marketplace interactions. Pretty standard stuff but the second part tripped me up more than I expected.
  • Given a table of buyer-seller listing interactions, how would you find the number of distinct sellers who, in the last 3 calendar days, have more than 3 products each with a listing interaction count greater than 1?
  • Among listings created in the US within the last 7 days, what percentage of total listing interactions comes from the 'Vehicle' category?

“Felt pretty comfortable here.”

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Meta·Software Engineer·Technical Phone Screen

Apr 2026
Went through a technical screen for an AI Research Scientist role at Meta. Heavy on ML fundamentals, the kind of stuff you think you know cold until someone's staring at you waiting for a precise answer.
  • Can you explain the bias-variance trade-off and how it affects model performance?
  • What are the differences between L1 and L2 regularization, and when would you choose one over the other? How does dropout fit into the regularization picture?
  • When would you use cross-entropy loss versus mean squared error, and what drives that choice?
  • Walk me through the key evaluation metrics for classification and regression tasks. How do you decide which metric matters for a given problem?
  • How do you set up train/validation/test splits, and when does cross-validation make more sense than a fixed split?
  • What are the main variants of gradient descent and how does learning rate scheduling factor into training stability?
  • How do you use learning curves to diagnose whether a model is underfitting or overfitting?

“Knew this one but fumbled the articulation.”

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Meta·Software Engineer·Technical Phone Screen

Apr 2026
Meta SWE interview, came across a coding question that was straight off LeetCode. Not much else to go on but it was a technical screen of some kind.
  • Given an integer array and an integer k, determine whether the array has a continuous subarray of at least two elements whose sum is a multiple of k (LeetCode 523).

“Prefix sum with modulo arithmetic.”

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Meta·Data Scientist·Onsite - Behavioral / Leadership

Apr 2026
Final behavioral round at Meta for a Data Science role. Two questions, both focused on how you handle things going sideways, either from change or from disagreeing with the data. Pretty standard cultural fit stuff but the second question has some teeth if you're not ready for it.
  • Tell me about a time you had to adapt quickly to an unexpected change. What did you do and what was the result?
  • Describe a situation where you disagreed with a decision that was backed by data. How did you handle it and what happened?

“I structured it as situation-action-result and leaned hard into the ownership angle, which felt right for Meta.”

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Meta·Machine Learning Engineer·Technical Phone Screen

Apr 2026
Meta MLE screen focused entirely on a single algorithmic problem, but they pushed hard on proof of correctness and a streaming extension that I wasn't fully ready for. Clean problem on the surface, messier under pressure.
  • Given an array of daily stock prices, find the maximum profit from at most one buy-then-sell transaction in O(n) time and O(1) space. Also prove your solution is correct and analyze its complexity.
  • Adapt the stock profit solution to a streaming setting where prices arrive one at a time and you must output the current best profit after each new price.

“The core algorithm wasn't the hard part.”

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