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Meta Software Engineer Interview Questions

Meta's SWE interview is famously fast-paced: 30-minute coding rounds where you ship 1-2 problems with optimal solutions. Behavioral rounds dig hard into impact and conflict.

Process length
4-6 weeks
Rounds
6
Questions
10
Mid-level TC
$280k–$380k (E4)
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The Meta Software Engineer interview process

What to expect, in order.

  1. 1Recruiter call (15 min, role + level fit)
  2. 2Phone screen (45 min — 1 coding problem, often medium difficulty)
  3. 3Onsite Coding 1 (45 min — 2 problems, must finish both)
  4. 4Onsite Coding 2 (45 min — 2 problems, harder difficulty)
  5. 5System Design (45 min, required for E5+)
  6. 6Behavioral / Jedi (45 min — impact, growth, conflict, collaboration)

What Meta actually evaluates

Meta engineers ship to production weekly and own outcomes end-to-end. The interview rewards candidates who can articulate impact in user/business terms, not just 'I wrote X service'. Behavioral rounds are blunt: be ready to discuss real conflict and real failure.

Impact — quantified business outcomes per quarter
Move fast — pragmatism over perfection
Be bold — risk appetite calibrated to upside
Focus on long-term impact — not just shipping

Process quirks worth knowing

Meta's coding rounds are notoriously tight: you must write working code on a shared editor, run it, and discuss optimality — all within 22 minutes per problem (45 min for 2 problems). Unlike Google, your interviewer carries more weight in the final decision; the hiring committee reviews but defers to onsite signal.

10 questions Meta actually asks

Each question includes the tip for answering and what the interviewer is actually evaluating.

Q1technical

Given a binary tree, find the kth smallest element using iterative in-order traversal.

Why Meta asks: Meta's 22-min-per-problem cadence means they pick problems with a clear 'optimal' answer. Iterative in-order forces you to know stack-based traversal, not just recursion.
How to answer: Use an explicit stack. Push left children until null, pop, decrement k, then go right. When k hits 0, you have your answer.
What they evaluate: Comfort with iterative tree traversal (no easy recursion shortcut), clean stack management, edge cases
Q2behavioral

Tell me about a project you owned end-to-end. What was the impact?

Why Meta asks: Meta's behavioral round (Jedi) deeply values quantified impact. 'I worked on the backend' won't pass — they want '$X revenue', 'Y million users', or 'Z% latency reduction'.
How to answer: Lead with the metric: 'I shipped X that drove Y% improvement in Z'. Then go into your specific contributions, the technical challenges, and what you'd do differently.
What they evaluate: Quantified business outcome, clear individual ownership (not 'we'), evidence of cross-team impact
Q3design

Design Instagram's news feed.

Why Meta asks: Meta interviewers expect you to discuss the fan-out problem (push vs pull), ranking signal infrastructure, and how to handle hot accounts (celebrities with millions of followers).
How to answer: Discuss the push (fan-out on write) vs pull (fan-out on read) tradeoff, then propose a hybrid: push for normal accounts, pull for celebrities. Cover ranking inputs (recency, engagement signals), the candidate generation layer, and ML inference latency.
What they evaluate: Knowledge of social graph problems, awareness of the celebrity-account edge case, ML serving knowledge
Q4technical

Merge k sorted linked lists.

How to answer: Min-heap of k pointers. Push the head of each list. Pop the smallest, append to result, push that node's next if non-null. O(N log k) where N is total elements.
What they evaluate: Recognition that heap beats naive O(Nk) merge, clean implementation of heap interactions, ability to discuss alternatives
Q5behavioral

Tell me about a time you had to deliver feedback to an underperforming teammate.

Why Meta asks: Meta promotes engineers partially based on people impact starting from E5. They want to see you can have hard conversations directly.
How to answer: Use a real example. Show you gave specific, behavioral feedback (not vague). Discuss the framework you used (SBI: Situation, Behavior, Impact). End with what the person did with the feedback.
What they evaluate: Comfort with direct conversation, structured feedback delivery, focus on the person's growth not your annoyance
Q6technical

Find the longest substring without repeating characters.

How to answer: Sliding window with a hash map storing the last index of each character. When you hit a repeat, move the window start to max(current_start, last_seen[char] + 1).
What they evaluate: Sliding window pattern recognition, careful index management, awareness of the 'last seen' optimization
Q7behavioral

Why are you leaving your current company?

How to answer: Frame in growth terms, not negative. 'I'm looking for X scale of impact and Y type of problem' beats 'My manager is bad'. Be honest but professional.
What they evaluate: Maturity in talking about previous experience, alignment between what you want and what Meta offers, no red flags about conflict handling
Q8design

Design a rate limiter for Facebook's API gateway.

Why Meta asks: Meta interviewers will probe distributed state — how do you enforce rate limits when traffic hits multiple gateway instances?
How to answer: Discuss token bucket vs sliding window algorithms. Then go distributed: centralized Redis vs sharded local counters with periodic sync. Cover the tradeoff between accuracy and latency.
What they evaluate: Knowledge of standard rate-limiting algorithms, understanding of distributed consensus tradeoffs, awareness of Redis as a common pattern
Q9technical

Implement word break: given a string and a dictionary, return whether the string can be segmented into dictionary words.

How to answer: Dynamic programming: dp[i] = true if s[:i] can be segmented. For each i, check all j < i: if dp[j] is true and s[j:i] is in the dictionary, set dp[i] = true.
What they evaluate: DP pattern recognition, comfort with 1D state, clear base case handling
Q10behavioral

Walk me through a time you made a wrong technical decision.

Why Meta asks: Meta probes self-awareness. They want to see candidates who own their mistakes and have a concrete lesson learned.
How to answer: Pick a real one with real consequences. Show: what you decided, why it was wrong, what you did when you realized, and the concrete lesson you carry forward.
What they evaluate: Self-awareness, ownership without excuses, evidence the lesson actually changed your subsequent behavior

Common ways candidates fail this interview

Specific to Meta, not generic interview advice.

  • ⚠️Solving only 1 of 2 problems in a coding round — Meta's bar is finishing both
  • ⚠️Behavioral answers without metrics — 'I improved performance' fails, 'reduced p95 latency by 40%' passes
  • ⚠️Confusing Meta's culture with Google's — Meta values 'move fast' and bold action, Google values 'cognitive depth' and analytical rigor
  • ⚠️Underprepping system design — even mid-level (E4) interviews now include design components
  • ⚠️Generic 'I want to leave my current job' answers — be specific about what you're looking for

Meta Software Engineer compensation (2026)

Entry / Junior
$180k–$230k total comp (E3)
Mid-level
$280k–$380k total comp (E4)
Senior+
$400k–$600k total comp (E5)

Sources: levels.fyi, Glassdoor, public filings (US figures, total compensation including base + bonus + equity).

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