Meta Software Engineer Coding Interview Guide

The Meta coding interview runs for about 45 minutes and is designed to look a lot like real engineering problem solving. You are evaluated on how you break down problems, how you translate ideas into structured code, and how clearly you explain your thinking, especially when things feel slightly unfamiliar.

To place this round in context with the full interview structure, visit the Meta Software Engineering Interview guide

You will typically work through two problems in 40 minutes using a shared editor with no code execution, which means minor syntax issues are expected and totally fine.

What matters more is whether your logic makes sense, whether your code reflects that logic, and whether you can catch and fix your own mistakes along the way.

You are free to code in whichever language you know best. Meta does not enforce a preferred language and uses a wide range of them internally. While scripting languages can help you express ideas faster, comfort and fluency always win over novelty. Choose the language that feels natural to you, and confirm with your recruiter just to be safe.

Meta evaluates coding interviews across four explicit focus areas, and these map very directly to how engineers are expected to operate once they are inside the company. We know this becasue it’s Prepfully Experts who are Software Engineers at Meta right now who have given us insight as to what these evaluation criteria are.

• Communication

At Meta, engineering work is rarely done in isolation. Problems are discussed, designs are reviewed, assumptions are challenged, and context is shared constantly across teams and time zones. Your coding interview is meant to mirror that reality in a small way. Interviewers listen for whether you ask clarifying questions, restate requirements, and align on the problem before you write code, the same way you would before touching production code that affects millions of users.

Talking through your approach is not about narration for its own sake, it is about showing that you can make your thinking legible to other engineers, which is essential when working on shared codebases and fast moving products.

• Problem solving

Meta engineers spend most days solving problems where the answer is not obvious, the requirements move mid-flight, and constraints show up from everywhere, including scale, performance, and real user impact. In the interview, this shows up in how you break the problem apart, explore more than one path, and talk through trade offs before locking anything in.

For example, you might start with a straightforward solution using a list, then realize a hash based structure makes lookups safer at scale, and then talk through why that choice improves time complexity without blowing up memory.

Interviewers care less about whether you land on the most clever solution immediately and more about whether your reasoning evolves as new constraints appear. They are listening for whether you can choose appropriate data structures, explain time and space complexity in plain terms, and justify why one approach fits the context better than another.

Optimization matters, but only after you have demonstrated correctness and solid structure.

• Coding

Coding at Meta is about making your intent legible to other engineers, not just to the compiler. In the interview, that shows up as writing code that is organized, readable, and logically structured, even though it is written in a plain text editor and never executed

For example, if you are solving a problem that involves scanning a list and tracking seen elements, a Meta interviewer would much rather see a clearly named hash map, a well structured loop, and an obvious separation between setup and logic, than a compressed one-liner that saves a few lines but hides intent.

A solution that is slightly less clever but easy to follow and maintain often scores better than a highly optimized one that feels dense or fragile. You are expected to optimize for readability and structure first, then performance where it matters, because code lives on and gets touched by many hands over time.

• Verification

Verification is one of the strongest signals of engineering maturity at Meta. Interviewers expect you to reason through test cases and edge cases on your own, the same way you would before pushing a change that others depend on.

For example, after writing a solution, a strong Meta candidate will often say something like, “Let me walk through an empty input,” or “What happens if all the values are the same,” or “What if this pointer hits the end early,” and then trace the logic carefully. If a bug surfaces, that is not a problem. What matters is whether you can explain why it happened, point to the exact line or assumption that caused it, and describe how you would fix it cleanly.

• Brief introductions and light context setting
• One or two coding problems focused on data structures and algorithms
• Coding in a plain text editor without execution, autocomplete, or debugging tools
• Ongoing discussion of requirements, assumptions, and trade offs
• Explicit reasoning about time and space complexity
• Manual walkthroughs of test cases and edge cases
• Time at the end for your questions

• Arrays and strings
• To see how you reason about boundaries, invariants, and transformations when constraints change mid-problem. Interviewers look for whether you can move comfortably between indexes, sliding windows, prefix logic, or in-place modifications, and whether you naturally sanity check edge cases like empty inputs, duplicates, or off-by-one conditions without being prompted.
• Hash tables and hash sets
• Meta interviewers watch for whether you reach for hashing instinctively when it simplifies a solution, and whether you can reason clearly about time and space trade offs. At this level, you are often expected to combine hash based structures with other techniques, like traversal or windowing, and explain why that combination makes the solution robust at scale.
• Stacks and queues
• Stacks and queues show up when the problem involves ordering, nesting, or controlled processing, and Meta uses them to test abstraction skills.
• Interviewers pay attention to whether you recognize when a stack models the problem more cleanly than ad hoc state, or when a queue naturally leads to a breadth-first approach. These questions often reward candidates who can articulate why a particular data structure mirrors the behavior of the system they are modeling.
• Trees, including binary trees and binary search trees
• Meta uses trees to evaluate recursive thinking, invariant maintenance, and how you manage state across calls. Interviewers look for clean traversal logic, correct base cases, and the ability to reason about depth, balance, or ordering properties, especially when the problem evolves or adds constraints partway through.
• Graphs
• Meta interviewers focus on how you handle visited state, cycles, traversal strategy, and correctness under changing conditions. Choosing between BFS and DFS, explaining why, and avoiding subtle bugs like infinite loops or missed nodes are key signals here. These problems also map closely to real Meta use cases involving dependencies, relationships, and reachability.
• Heaps
• Heaps tend to show up when the problem involves prioritization, streaming data, or maintaining a dynamic top-K view. Meta uses these to see whether you understand how to maintain order efficiently over time, rather than recomputing from scratch. Interviewers care about whether you can justify reaching for a heap and talk through how its performance changes the solution.
• Recursion and iteration
• Meta interviewers look for whether you understand call stacks, termination conditions, and how state flows through recursive calls. Just as important is knowing when to switch to an iterative approach and being able to explain that trade off clearly, especially when recursion depth or stack limits become relevant.
• Sorting
• Meta cares about whether you recognize when sorting simplifies a problem, what it costs in time and space, and how it changes downstream logic. Interviewers often probe whether you can reason about sorted data to unlock simpler solutions, rather than treating sorting as an automatic first step.
• Divide and conquer techniques
• These problems test whether you can decompose a problem into smaller, independent pieces and combine results cleanly. Meta uses divide and conquer to evaluate problem decomposition, recursive structure, and correctness across boundaries. Clear reasoning about how subproblems relate to the whole is more important than arriving at the most optimized version immediately.

Meta does not ask dynamic programming in this round, on purpose. The focus is on reasoning, structure, and judgment with common data structures, not on memorizing DP patterns. Your prep time is better spent getting fluent with the fundamentals and learning how to combine them well.

Plan your prep with a Meta SWE who has navigated this interview successfully.

Over 2,800 candidates preparing for this interview have already used this leverage. It’s time to catch up.

It beats asking your browser and stitching together recycled advice every time. It helps you double down on strengths, fix the right gaps, and avoid the kind of prep that just burns time.

• Find the longest substring that contains at most k distinct characters.
• Return the top k most frequent elements from a stream of integers.
• Determine whether a binary tree is height balanced and explain why.
• Find the lowest common ancestor of two nodes in a binary tree when parent pointers are not available.
• Merge overlapping intervals and explain how the ordering affects correctness.
• Detect a cycle in a directed graph and explain how you would surface it.
• Design a data structure that supports insert, delete, and getRandom in constant time.
• Compute the shortest path between two nodes in an unweighted graph.
• Find the longest increasing subsequence and reason about the optimal approach.
• Implement an LRU cache and walk through how eviction works under load.
• Find all anagrams of a pattern inside a larger string.
• Return the k closest points to the origin using an efficient approach.
• Determine if a graph is bipartite and explain how you detect conflicts.
• Flatten a binary tree into a linked list in place.
• Find the minimum window in a string that contains all characters of another string.

See what candidates are being asked in Meta SWE interviews right now

• Start by making sure you and the interviewer are solving the same problem. Ask clarifying questions, restate the requirements, and confirm constraints before you touch the keyboard. If the problem looks familiar or you have seen a close version before, say that out loud so the interviewer has the right context.
• As you think through the solution, try to talk through more than one reasonable approach. Meta interviewers care about how you compare options, reason about trade offs, and decide what makes sense for the problem at hand, not just whether your first idea works.
• You should be comfortable reaching for common data structures like arrays, stacks, queues, hash maps, trees, heaps, and graphs, and explaining your algorithm choices using Big O notation in simple terms. The goal is to show that you understand how your choices behave as inputs grow.
• While you are coding, keep narrating what you are doing and why. The interviewer is evaluating your reasoning in real time. If you get stuck or hit a fork in the road, explain the direction you are leaning toward and check in, rather than going quiet or trying to solve it in your head.
• Aim for clear, straightforward solutions. Simple, iterative approaches usually land better than overly clever ones, especially if they are easier to explain and reason about. If an approach takes a long time to explain, it is often more complex than the problem requires.
• Expect the interviewer to ask follow-up questions or adjust the problem. They may add constraints or ask how you would optimize further. Treat this as a collaborative moment, since these questions are meant to surface how you adapt, not to trip you up.
• Before finishing, do a quick self-review. Walk through correctness, efficiency, and edge cases, and talk about whether you would be comfortable approving this solution in a real codebase.

If you have ever thought, “I know this, why is it not coming through in interviews?”, nobody better than a Prepfully coach at Meta to answer that.

With an average rating of 4.85 across 18000+ sessions, we are comfortable saying we know what we are doing.

• Schedule time to study and practice with mock interviews

A little bit of coding every day goes much further than long, stressful weekend marathons. Meta’s interviews expect you to write code without running it, so practice in a plain text editor with no compiler, no debugger, no autocomplete. It feels uncomfortable at first, and that is the point. You are training your brain to reason instead of leaning on tools.

Give yourself a worthwhile shot with mock interviews on Prepfully.
This is the only place your mistakes get to be productive, so they do not get expensive later, costing you offers.

• Prioritize breadth over depth

It is tempting to get very good at one kind of problem and call it progress. Meta will gently punish that instinct. You are much better off solving a smaller number of problems across many categories, even if each one feels a bit rough. Meta interviews reward pattern recognition and adaptability, not mastery of a single trick. A decent grip on arrays, trees, graphs, and hashing beats deep comfort in only one lane.

• Think in techniques

Focus on core techniques like iteration, sorting, divide and conquer, and recursion, and get comfortable switching between them when the problem nudges you to. Meta does not ask dynamic programming in this round, which is a gift. You can stop hoarding DP templates and spend that time learning how to combine simpler ideas thoughtfully. Interviewers care much more about how you reason than whether you remember a clever formula.

• Be fluent with the basics that show up everywhere

You should feel at ease using arrays, stacks, queues, hash tables, trees including binary trees and binary search trees, graphs, and heaps. These show up again and again because they map cleanly to real engineering problems. Meta interviewers are watching to see whether you reach for the right abstraction naturally and whether you understand how that choice affects performance as things scale. If you ever find yourself forcing a data structure into a problem, it is usually a sign to step back.

• Practice in the exact environment you will interview in

The interview happens in a plain text editor with no safety nets, so practice that way. It will feel awkward, and you will miss things you normally rely on, and that is fine. Also practice talking through your approach before you start coding. Meta interviewers are evaluating how you explore the problem space and weigh options, so your reasoning needs to be out in the open, not locked in your head. A practice interview is as close as it gets to the “exact environment”. Over 2,800 candidates took that route and left stronger than they walked in.

Browse for a Meta SWE to simulate this environment

• Keep it simple

If your solution starts to spiral into something hard to explain, that is usually your cue to pause. Meta tends to reward candidates who step back, simplify, and choose a cleaner approach over those who push through a complicated one. Writing things out clearly often helps you catch bugs faster and reduces careless mistakes. Simple, readable solutions age better, both in interviews and in real codebases.

Interview prep:
Meta Software Engineer Interview Guide

Meta Software Engineer Initial Interview Guide

Meta System Design and Product Architectural Design Interview Guide

Meta SWE Interview Question bank with answer reviewing tool

Meta SWE Mock Interview Coaches

Role specific prep:

Meta Software Engineer Interview Guide

Meta Senior vs Staff Engineer Interview Expectations

Understanding the AI assisted coding round

Engineering at Meta

Meta Code videos

Meta Opensource website

Engineering Leadership at Meta blog

Coding Resources:

Meta’s Software engineering interview Q&A

Cracking the Meta Coding Interview Videos: The Approach and Problem Walkthrough (password:FB_IPS)

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