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Meta interview notes, page 164 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

Jun 2026
Meta PM interview with a single product improvement question about Facebook Marketplace. Not a lot of context to go on here, but it's a pretty classic Meta product sense prompt.
  • How would you improve Facebook Marketplace?

“Classic Meta product sense question and I still felt underprepared.”

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Meta·Data Scientist·Technical Phone Screen

Jun 2026
Meta DS interview that went deep into ML evaluation for a fake account detection problem. The whole thing was basically one long case question with a bunch of sub-parts, and I was not fully prepared for how much math they expected me to do on the spot.
  • You suspect fake users are inflating comment counts. You need to build a classifier to flag them for review. Given severe class imbalance and a hard daily review capacity, which offline evaluation metrics would you choose and why? When would you prefer PR-AUC over ROC-AUC?
  • Given a population of 10 million daily active users with a 1% true fake rate, compute the expected true positives and false positives per day for two models: Model A with precision 0.60 and recall 0.20, and Model B with precision 0.20 and recall 0.80. Does either model fit within a 50,000 account/day review capacity?
  • Given that a false positive costs $2 (review cost) and a false negative costs $100 (missed abuse), which Fβ score would you choose and why? Calculate the expected daily cost under both models at their current thresholds.
  • How would you choose a classification threshold using a precision-recall curve, subject to a constraint of either precision at or above 0.7 or fewer than 20,000 false positives per day? How does probability calibration fit into this process?
  • Describe a validation approach that avoids data leakage for this kind of temporal abuse detection problem. What offline-to-online guardrails would you use, and which online metrics would you track post-launch?

“This is where I spent the most time and probably where I lost the most points.”

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Meta·Data Scientist·Technical Phone Screen

Jun 2026
Stat-heavy technical screen for a DS role at Meta. The whole thing revolved around one multi-part experiment design question and they really wanted you to go deep on the math, not just name-drop CUPED.
  • A pre-period covariate explains 36% of the variance in your outcome metric. Walk through how covariate adjustment (e.g. CUPED-style) changes the variance of your treatment effect estimator, and quantify the sample size savings you'd expect.
  • What assumptions does CUPED rely on, and under what conditions would it break down? What diagnostics would you run to check?
  • Given this setup, would you prefer stratified randomization or post-hoc covariate adjustment, and what's your reasoning?

“The 36% number is a gift because it maps directly to variance reduction.”

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Meta·Machine Learning Engineer·Onsite - Behavioral / Leadership

Jun 2026
Meta MLE interview that went pretty deep on two fronts: leading a team through reorg chaos while still shipping ML work, and basically a full audit of how you handle user data compliance end to end. Not a casual conversation.
  • Tell me about a time you led a team through a reorganization while still delivering on an ML roadmap. How did you handle scope changes, timeline resets, stakeholder communication, and keeping the team motivated? What tradeoffs did you make and how did you measure whether things went well?
  • Walk through how you ensure data compliance across ML pipelines that use user data, covering things like PII minimization, consent and purpose limitation, GDPR and CCPA requirements, data retention and deletion, user data access request workflows, audit logging, DLP and redaction, sandboxing, vendor reviews, data lineage, and preventing sensitive data from leaking into training or evaluation sets.

“This one is deceptively big.”

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Meta·Software Engineer·Onsite - System Design / Architecture

Jun 2026
System design round at Meta for a software engineer role. The prompt was to build an auction layer on top of Instagram, which sounds fun until you realize how many moving parts there are: real-time bid updates, consistency guarantees, flash-sale traffic spikes, and anti-abuse all in one go. Dense two hours.
  • Design an online auction system built on top of Instagram, where sellers can post items with a starting price and end time, and buyers can place bids in real time.
  • How would you handle concurrent bids arriving at the same time, and what's your tie-breaking strategy?
  • Walk through your approach to real-time bid fan-out. Would you use WebSockets, server-sent events, or polling, and why?
  • What are the non-functional requirements you'd prioritize for this system, specifically around latency, consistency, and availability during traffic spikes?
  • How would you design the API and data model for this auction system?
  • What anti-abuse and fairness mechanisms would you build into the auction system?
  • How would you scale and partition this system to handle a large number of concurrent auctions?

“This is the main prompt and it ballooned fast.”

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Meta·Product Manager·Onsite - Behavioral / Leadership

Jun 2026
Went through a behavioral round for a PM role at Meta. Just the one question from what was shared, but it was the kind that sounds easy until you're actually sitting there trying to remember a real example that doesn't make you look terrible.
  • Tell me about a time when an employee gave you negative feedback.

“I fumbled this a bit.”

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Meta·Software Engineer·Onsite - Coding / Algorithms

Jun 2026
Meta SWE coding round, one question the whole time, grid BFS. Felt pretty focused but also like they were waiting for me to say the magic words about multi-source traversal.
  • You're given a 2D grid where cells are either walls, gates, or empty rooms. Fill each empty room with the shortest distance to the nearest gate using four-directional movement. Walk through your approach, justify why you'd use multi-source BFS over other strategies, and analyze time and space complexity. Also handle edge cases like multiple gates, fully enclosed rooms, and large grids.

“I started with the naive thing, single-source BFS from each gate separately, and the interviewer let me finish before asking what the complexity was.”

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Meta·Data Scientist·Technical Phone Screen

Jun 2026
A deep-dive product analytics case for a DS role at Meta, all centered on designing a measurement framework for a customer-support chatbot. Single question but it had five sub-parts and honestly felt more like a take-home crammed into a live session.
  • You own a B2C customer-support chatbot aimed at reducing agent handoffs. Design a complete success framework: define a primary KPI that balances containment and customer utility (including its formula, eligible population, attribution window, and how to handle abandoned chats and recontacts within 72 hours), at least three guardrail metrics with thresholds, an experiment design covering randomization unit and seasonality, the instrumentation and logging you need, and a quasi-experimental fallback if randomization is not feasible.

“This was basically five questions glued together and they expected a coherent answer, not five separate answers.”

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Meta·Software Engineer·Technical Phone Screen

Jun 2026
Meta data science interview with a classic product growth question. Pretty short on details but the core ask was about DAU improvement for Facebook, which is the kind of open-ended thing that can go a dozen directions.
  • What strategies would you propose to improve Facebook's daily active users?

“I went straight to retention and re-engagement because acquisition felt too obvious, but looking back I probably should have structured it more before diving in.”

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Meta·Software Engineer·Onsite - Coding / Algorithms

Jun 2026
Meta SWE coding round, one question on array range filtering. Pretty straightforward but worth knowing the edge cases.
  • Given an unsorted integer array and two boundary values S and E, return the sum of all elements that fall within the inclusive range [S, E].

“Pretty simple linear scan, just iterate and accumulate when the element is between S and E.”

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