Interview Cake

Meta Interview Guide

Landing a software engineering role at Meta is a coveted goal for many developers—perhaps this is your goal, too! But to get to that awesome job offer, you’ll have to pass a number of interviews known for their intense time pressure. Preparing can seem daunting, and it’s easy to get lost in Reddit threads, stories from friends, and blog posts. Where do you even begin?

This article is designed to be your focused and actionable guide through Meta’s interview process. We'll move beyond generic interview advice and focus on what really matters for Meta coding interviews: effective communication and implementation speed. Drawing directly from Meta’s official interview guides, we’ll pinpoint specific concepts to master, highlight effective practice strategies, and set you up for success. Remember, preparation is crucial, and with the right approach, a Meta offer letter is absolutely within your reach.

Let's begin.

Jobs at Meta: Roles and Levels

Meta hires across a wide spectrum, from hands-on coding roles to those focused on strategy and product vision. The first part of your interview preparation is to figure out what kind of role you’d like to apply for. Broadly, Meta’s technical positions fall into a few different categories:

Software Engineering: Jobs where your primary responsibility is writing code.

  • Software Engineer (SWE): These individuals are the builders, responsible for designing, developing, testing, and deploying software. This can range from front-end development for user-facing applications to back-end infrastructure that powers the platform.
  • Specialized Software Engineers: Within SWE, some roles are specialized to particular domains:
    • Machine Learning (ML) Engineer: Developing and deploying ML models for various applications, such as news feed ranking, content recommendation, and fraud detection.
    • Security Engineer: Focusing on protecting Meta's systems and user data from cybersecurity threats.
    • Data Engineer: Building and maintaining the data pipelines and infrastructure necessary for data analysis and decision-making.
    • Front-End Engineer: Specializing in the user interface and user experience of Meta's products.
    • Back-End Engineer: Focusing on the server-side logic, databases, and APIs that power Meta's applications.

Product Management (PM): Product Managers are the strategists and visionaries who define the "what" and "why" behind Meta’s products:

  • Product Manager (PM): Identify user needs, define product roadmaps, and work closely with engineering, design, and other teams to bring products to life. They are responsible for the overall success of their product area.
  • Technical Program Manager (TPM): Focus on the "how" of product development. They drive complex technical projects, manage timelines, and ensure smooth execution across engineering teams. They often have a strong technical background, while their primary focus may not be on writing code.

Levels at Meta

Meta uses a numerical system (E-levels) to denote experience and responsibility across many technical roles, particularly within engineering. Based on your professional background and experience, you’ll likely be aiming to enter Meta at a specific level.

  • E3 Entry-Level / Associate: This is typically the starting point for new graduates or those with limited professional experience. The focus is on learning, contributing to specific tasks, and developing foundational skills.
  • E4 General: At this level, individuals are expected to be more independent, take ownership of larger pieces of work, and contribute significantly to team goals. They demonstrate a solid understanding of their domain.
  • E5 Senior: Senior roles involve leading projects, mentoring junior engineers/PMs, and making significant technical or product decisions. They have a deep understanding of their area and can drive initiatives with minimal guidance.
  • E6 Lead: Leaders at this level have a broader impact, influencing the direction of teams or entire product areas. They are expected to be strategic thinkers, excellent communicators, and capable of driving significant impact across the organization.

The focus and difficulty of your technical interview will vary depending on the level of the role you’re applying for. In general, E3 and E4 interviews tend to focus more on coding and implementation, while E5 and E6 interviews will have a heavier emphasis on system design. Focus your interview preparation accordingly.

Meta’s Job Application Process

Applying Online

The first step towards landing a job at Meta is crafting a compelling resume. While it's tempting to use a generic resume for multiple applications, tailoring your resume to specific roles that align with your skills and experience is highly recommended. Ensure your resume is clear, concise, and effectively showcases your accomplishments and relevant work experience.

As you browse the Meta Careers site, look for roles that catch your eye. Once you find suitable positions, submit your tailored resume. This sends your application to a Meta recruiter, who will review the submissions for the role. If they think you’re a good match, you’ll move on to the next stage: an initial recruiter screening call.

Recruiter Screening Call

These typically last around 30 minutes and cover topics like:

  • Your background — providing additional detail on your professional experiences
  • Your goals — explaining your motivations for joining Meta and your interest in the specific role
  • The position — understanding the role’s fit within a team and Meta’s larger organization
  • The application — clarifying next steps in the interview process

Be prepared to answer questions like:

  • Tell me about yourself and your professional background?
  • Why do you want to join Meta? What interests you about this job?
  • Do you have any experience leading teams? Designing systems?

You probably won’t be asked any technical questions in this call. The primary goal at this stage is establishing that the role is a match for your professional background and that you’d do well on follow up technical interviews. At the end of the conversation, the recruiter will ask if you’d still like to continue with the application. Think carefully and answer honestly:

  • If the role doesn’t seem like a good fit, that’s perfectly fine. Keep looking for other jobs that are a better match for your background and goals. You can even ask the recruiter if they have specific teams or positions they’d recommend based on your profile.
  • If the role seems awesome, you’ll work with the recruiter to schedule your technical screening.

The Initial Technical Screen

This next step connects you directly with a member of the hiring team — potentially one of your future coworkers!

These conversations typically last 45 minutes, structured as follows:

  • First ~5 minutes: Discussion of the role and why you’re a good fit for it
  • Core ~30 minutes: Solve two technical coding challenges
  • Final ~5 minutes: Time for you to ask questions about the role and the team

We’ll cover specifics of the technical coding challenge a bit later. For now, just know that this step tests your knowledge of fundamental data structures and algorithms. You’ll either code in a shared text document (for remote interviews) or on a whiteboard (for in person interviews). Since this can be a bit awkward at first, be sure to practice ahead of time.

Note the significant time constraint! Solving two problems in 30 minutes requires speed and efficiency. Make sure you’re practicing challenges with a timer, so you can confidently pace yourself during the interview.

If you pass the initial technical screen, the recruiter will follow up to schedule another round of interviews: the "full loop."

The Full Loop Interview at Meta

This stage is a more in-depth series of interviews testing your technical expertise, interpersonal abilities, and system design knowledge. Typically, you’ll be scheduled for a day’s worth of 45-minute interviews.

These interview fall into three categories:

Design Interviews

Depending on the position you’re applying for, you’ll either have a systems design interview or a product architectural design interview. Ask your recruiter which one you should expect, so you can prepare effectively.

  • In a systems design interview, you’ll be given a scenario and tasked with designing a scalable distributed system. Generally, these conversations start with a simpler version and then become increasingly complex in order to maintain high performance as the system scales up.

    You should be prepared to talk about topics like:

    • Scalability — how the system grows to meet demand from a global user base
    • Security — how the system is robust to attacks and authenticates user actions
    • Usability — how users seamlessly interact with the system (e.g.: intuitive interfaces, error messages)
    • Extensibility — how the system can grow to incorporate new features

  • In a product architectural design interview, you’ll design an API for interacting with an existing system. You’ll have to figure out the right interfaces — balancing usability, functionality, security, and scalability.

    Expect to discuss topics like:

    • Scalability — how the API will handle lots of requests efficiently
    • Protocols and data formats — how the API receives and returns data (e.g.: JSON, REST)
    • Storage models — how the system represents internal data and exposes it to users
    • Statefulness — how users are authenticated, request idempotence, and server-side state management

Practice:

Behavioral Interviews

These interviews assess your ability to collaborate effectively and solve technical challenges within a team setting. Your goal is to demonstrate how you’d seamlessly fit into the team and Meta overall.

Typically, you’ll be asked to describe situations where you:

  • Learned something new and applied it to a real-problem, resulting in tangible improvements:
    • What’s a new technical topic you’ve gotten better at in the past year? How have you used it?
    • How do you stay current in the field?
  • Worked through disagreements with colleagues to reach a positive and productive resolution:
    • Talk about a time you had a disagreement with a technical peer? How did you work through the disagreement, and what was the end result?
    • What would you do if you were leading a team and two of your team members repeatedly clashed with each other?
  • Collaborated effectively within and across teams to get things done:
    • Talk about a time when you’ve built something on top of another team’s systems. How did you interact with that team? Did you need them to make any changes?
    • Have you ever worked on a project that required multiple people? How did you fit into the larger team?
  • Made sound decisions in complex or fluid situations, even with incomplete information:
    • What would you do if you were two weeks from finishing a new feature and then priorities shifted, meaning the feature wasn’t needed any more?
    • Can you talk about a time when you’ve worked in a quickly-changing environment? How did you stay on top of the changes?

It’s best to brainstorm specific examples ahead of time. Think about your prior work experiences and practice concisely describing the scenario, your role, and your contributions. The S.T.A.R method (Situation, Task, Action, Result) can be helpful for structuring your anecdotes in an easy-to-follow way.

For more tips, check out our behavioral interview guide.

Coding Interviews

These interviews are similar in format to the initial technical screening. You’ll have 30 minutes to answer two coding questions using foundational data structures and algorithms.

Your goal during these interviews is to showcase your ability to:

  • Communicate - clearly articulate your thought process and algorithmic choices to others
  • Solve Problems - analyze a given task and figure out efficient data structures and algorithms to solve it
  • Code - transform your ideas into clean, readable, and maintainable code
  • Debug - identify edge cases and bugs through testing and code review

Expect the problems to be more challenging than those in the phone screening. You’ll definitely want to practice problems that are harder ahead of time. When practicing, code the same way you’ll be coding during the interview: writing on a whiteboard or a plain text editor.

Let’s look at some specific topics you’ll want to master to confidently approach these interviews.

Core Concepts and Skills to Master for Meta’s Technical Interviews

Meta coding interviews test your ability to apply fundamental computer science principles to solve problems efficiently. While the specific questions will vary, the underlying concepts remain consistent, so mastering core data structures, algorithms, and analytical skills is crucial. Let’s break down the key building blocks.

Data Structures: The Foundation

A solid understanding of data structures is crucial for building efficient algorithms. You should be confident working with:

  • Lists (Arrays, Vectors): Contiguous blocks of memory storing elements of the same type. Index-based access is ; searching or removing elements is .

    Practice:

    • Apple Stocks: Given the price history for a stock, determine the time to buy and sell that maximizes profit.
    • Find Rotation Point: A few items from the front of a sorted list have shifted around to the back. Figure out where the original starting point in the list ended up.

  • Linked Lists: Data structures where each node contains data and a pointer to the next node. Adding elements on either end is , but accessing an item in the middle of the list is .

    Practice:

    • Delete Node: Given a pointer to a node in a linked list, remove that node in constant time.
    • Linked List Cycle: Determine if a linked list has a cycle, and find where the cycle starts. This one has an elegant solution!
    • Reverse a Linked List: A pointer manipulation classic: given a linked list, flip it around!

  • Stacks: Last-In, First-Out data structure. Elements are pushed onto and popped from the top. All operations are , but only the top element is directly accessible.

    Practice:

    • Largest Stack: Implement a stack with an extra feature — O(1) retrieval of the largest element in the stack.

  • Queues: First-In, First-Out data structure. Elements are added to the rear and removed from the front. All operations are , but only the front element is directly accessible.

    Practice:

  • Hash Tables (Hash Maps, Dictionaries): Data structures that organized data as key-value pairs. Average-case operations are assuming minimal hash collisions; worst-case performance degrades to .

    Practice:

    • Word Cloud: Count how many times a word appears in a text. While seemingly straightforward, this problem highlights some complexities of natural language processing.
    • Permutation Palindrome: Given a word, figure out if any permutations of the letters create a palindrome.
    • Inflight Entertainment: Given a set of movies, is it possible to choose two movies whose total runtimes will add up to the flight length?

  • Binary Trees: Hierarchical data structures where each node can have up to two children (left and right). In Binary Search Trees (BSTs), nodes to the left are smaller and nodes to the right are larger, enabling efficient lookups and insertions. Balanced BSTs (like AVL or red-black trees) guarantee performance by maintaining balanced subtrees.

    Practice:

  • Graphs: Data structures representing relationships between entities (nodes or vertices) through connections (edges). Common operations include pathfinding, minimum spanning tree algorithms, and graph coloring.

    Practice:

    • Graph Coloring: Assign colors to nodes in a graph while ensuring adjacent nodes have different colors.
    • Mesh Message: Find the shortest path for messages to take in a peer-to-peer cellular network.

  • Heaps (Priority Queues): Tree-based data structures where the root node holds the smallest (or largest) value, maintaining a partial ordering. Heaps are efficient for sorting (heap sort) and are frequently used in problems involving finding the kth smallest/largest elements or ordering items based on some priority value.

Key Algorithms

Algorithmic techniques are fundamental problem-solving strategies that apply to lots of coding challenges.

  • Sorting Algorithms Techniques for arranging elements in a specific order (ascending or descending).

    Efficient algorithms like Merge Sort or Quick Sort are . Simpler algorithms like Selection Sort or Insertion Sort are . Be comfortable implementing at least one efficient sorting algorithm, and understand how efficient algorithms differ: merge sort is stable, and quick-sort operates in place.

    Practice:

    • Merge Sorted Lists: Combine two smaller sorted lists into one bigger sorted list. This one seems simple, but don’t forget about all the edge cases!
    • Top Scores: Sort a list of numbers in better than time

  • Searching Algorithms: Techniques for locating a specific element within a data structure.

    • Linear Search: Sequential traversal through the elements, O(n) time complexity.
    • Binary Search Efficiently searches sorted lists or data structures in time. The data must be sorted for this to work!

    Practice:

  • Recursion: A problem-solving technique where a function calls itself to solve smaller, similar subproblems.

    Key concepts:

    • Base case: the stopping condition that prevents infinite recursion
    • Recursive step: reducing the problem to a smaller instance.
    • Space overhead of the recursive call stack: Often this boosts the space complexity of recursive algorithms!

    Recursion is particularly prevalent in tree and graph problems.

    Practice:

  • Divide and Conquer: A problem-solving pattern that breaks a problem into smaller, similar subproblems, solves them recursively, and then combines the solutions to solve the original problem. The general divide and conquer approach often leads to efficient algorithms.

    Merge Sort, Quick Sort, and Binary Search are all examples of divide and conquer algorithms.

    Practice:

    • Find rotation point: A few items from the front of a sorted list have shifted around to the back. Figure out where the original starting point in the list ended up.

  • Greedy Algorithms An approach where you make the locally optimal choice at each step, hoping to achieve the globally optimal solutions.

    Greedy algorithms are not guaranteed to find the optimal solution for all problems, but they are effective in some cases. Understanding when a greedy approach works (or doesn’t) is crucial.

    Practice:

    • Merging Ranges: Given a set of calendar appointment blocks, find a time when everyone is available to meet.
    • Highest Product of Three: Find the highest possible product that can be made using exactly three items from the a list.

Important Note: Meta explicitly states: "We do not ask dynamic programming questions so don’t spend time prepping for that technique and focus your efforts elsewhere." While dynamic programming is powerful, prioritize mastering the other concepts.

Time and Space Complexity Analysis (Big-O Notation)

Big-O notation is the standard mathematical notation to describe the time and space complexity of algorithms. This quantifies how runtime and memory usage scale as the input size grows. Interviewers will nearly always ask you to determine the time and space complexity of your algorithms.

  • Understanding Big-O: Big-O notation focuses on asymptotic behavior — how performance changes with very large inputs. It emphasizes the dominant term and ignores constant factors and lower-order terms to provide a simplified representation of the growth rate.

  • Common complexity classes:

    • — Constant time: Runtime remains constant regardless of input size (e.g., list access by index, hash table lookups).
    • — Logarithmic time: Runtime grows logarithmically with input size (e.g., binary search). Often associated with algorithms that repeatedly divide the problem size in half.
    • — Linear time: Runtime grows proportionally to the input size (e.g., linear search, traversing a linked list).
    • : Runtime grows slightly faster than linear (e.g., efficient sorting algorithms like Merge Sort and Quick Sort in average case).
    • — Quadratic time: Runtime grows quadratically with input size (e.g., nested loops, iterating through all pairs of elements).
  • Space Complexity: Measures the amount of memory an algorithm uses as a function of the input size.
  • Space-Time Trade-offs: Often, you can improve the time complexity of an algorithm by using more space, or vice-versa. Understanding these trade-offs is important for making informed design decisions and a common interview discussion topic.

Practice Complexity Analysis: Don't just memorize complexities. Practice analyzing the time and space complexity of your own code and solutions to practice problems. Being able to do this confidently and quickly keeps you on pace to solve two problems in the 30 minutes allotted.

Effective Preparation Strategies

Knowing what to study is only half the battle. How you study is equally, if not more, important. Effective preparation isn't just about grinding through countless problems; it's about strategic planning, targeted practice, and simulating the actual interview environment.

Structured Study Plan and Time Management

Like any complex project, successful interview preparation requires a structured plan and disciplined time management.

  • Assess Your Strengths and Weaknesses: Begin with an honest self-assessment, so you can focus your preparation on topics where you need it most.
  • Create a Study Schedule: Based on your self-assessment, create a realistic study schedule. Allocate time blocks for specific topics, practice problem sessions, and regular review. Break down your preparation into manageable chunks – daily or weekly goals. Be realistic about how much time you can dedicate each day and stick to the schedule as closely as possible.
  • Prioritize Breadth over Depth: Focus on covering a wider range of problem types and fundamental concepts rather than becoming hyper-specialized in a narrow area. Remember, interviewers can ask you about anything; you’ll want to be prepared for whatever topic they choose.
  • Consistent Practice is Key: Regular, consistent exposure reinforces concepts and builds muscle memory for problem-solving. Aim to do an hour or two of focused practice each day.

Practice Methods and Techniques

When you’re practicing, simulate the actual interview environment to hone your skills.

  • Practice Coding the Way You'll Code: Use a blank text document if your interview is via phone or video call, or use a whiteboard or pen and paper if your interview will be in person. This is crucial. You won’t have autocomplete or built-in debugging features, so make sure to practice solving problems without them.
  • Time-Constrained Practice: The time pressure in Meta interviews is real. During practice sessions, strictly adhere to time limits. Aim to solve two problems within 30 minutes, using a timer to track your progress and build speed and efficiency. This will help you become comfortable working under pressure and make quick, effective decisions during the actual interview.
  • Thinking Aloud Practice: Communication is key. During your preparation, always practice thinking aloud. Verbalize your thought process, explain your understanding of the problem, and justify your data structure and algorithm choices. Even during solo practice, speak your thoughts out loud as if you were explaining to an interviewer. Record yourself practicing and review the recordings to identify areas where your explanations could be clearer or more concise.
  • Simulating the Interview Environment: Beyond solo practice, simulate the full interview experience. Ask friends or peers to conduct mock interviews with you. Have them ask you coding questions and practice explaining your approach and coding while they observe and provide feedback.

Mastering the Interview Itself: In-Interview Strategies

Preparation lays the groundwork, but your performance during the interview determines the outcome. Let’s talk about strategies to put your best foot forward during each 45-minute conversation.

Before You Start Coding: Clarity and Communication are Paramount

The initial moments of the interview are crucial for setting the right tone and ensuring you fully understand the problem. Rushing into coding without proper clarification is a common mistake that can lead to misdirection and wasted time.

  • Active Listening and Clarifying Questions: The first and most important step is to actively listen to the interviewer's problem description. Pay close attention to constraints, input/output formats, and any specific requirements mentioned. Don't hesitate to ask clarifying questions. Proactive questioning demonstrates engagement and a thoughtful approach.

    Examples of Good Clarifying Questions:

    • "Could you provide an example input and the expected output to ensure I understand the problem correctly?"
    • "What are the constraints on the input size (e.g., maximum length of the list, possible rangees of parameters)?"
    • "Are there any performance requirements or memory limitations I should be aware of?"
    • "Should I assume the input is always valid, or do I need to handle potential invalid inputs?"
  • Be Honest: If you recognize the problem and have already solved it, be upfront about it. Trying to feign originality when you already know the solution comes off as awkward, and transparency builds trust.
  • Present Multiple Potential Solutions: Before diving into code, briefly outline your initial thoughts and potential approaches. If you can think of multiple ways to solve the problem (e.g., a brute-force approach and a more optimized one), briefly mention them. Explain the trade-offs of each approach (e.g., time complexity, space complexity, implementation complexity) and justify why you are choosing to pursue a particular approach first. This demonstrates your ability to analyze different solutions and make informed decisions.

During Coding: Thought Process and Execution

Once you have a clear understanding of the problem and have outlined your initial approach, it’s time to start coding.

  • Don't Forget to Talk!: This cannot be stressed enough. Continuously verbalize your thought process while you are coding. Explain why you are writing each line of code, why you are choosing specific data structures or algorithms, and what you are trying to achieve at each step. Imagine you are pair programming and explaining your code to a colleague. This "thinking aloud" allows the interviewer to follow your logic, understand your reasoning, and provide guidance or hints if you are heading in the wrong direction. Silence, even while coding correctly, can be detrimental as it deprives the interviewer of valuable insights into your problem-solving abilities.
  • Iterate, Don't Jump to the "Clever" Solution: It's often better to start with a simpler, more straightforward solution (even if it's not the most optimal) and get a working version quickly. Then, if time permits and you have a better approach in mind, iterate and optimize. This demonstrates a pragmatic approach and the ability to improve upon a solution.
  • Handle Edge Cases Early: Think about potential edge cases and boundary conditions early in your coding process. Address them proactively rather than as an afterthought. This demonstrates attention to detail and robustness.
  • Write Clean, Readable Code: Focus on writing code that is not only correct but also clean, well-organized, and easy to understand. Use meaningful variable names, break logic into modular functions, and add comments judiciously.
  • Manual Testing and Debugging: Once you have written a solution, manually test your code with example inputs. Step through your code line by line, mentally tracing the execution and checking for errors. If you find a bug, don't panic. Explain your debugging process to the interviewer and fix the code, showcasing your troubleshooting abilities.
  • Time Management During the Interview: Keep an eye on the time. If you are spending too long on a particular part of the problem or are stuck on a specific issue, don't hesitate to communicate with the interviewer. The interviewer might offer a hint or suggest a different direction to keep the conversation moving forward.
  • Complexity Analysis: Explicitly state the time and space complexity of your solution. Explain why your solution has that complexity, referencing the algorithms and data structures you used. If you have time, discuss potential optimizations and how they might affect complexity.
  • Responding to Interviewer Hints and Feedback: Be receptive to any hints, suggestions, or feedback the interviewer provides throughout the interview. Actively listen to their input and incorporate their suggestions into your solution. Show willingness to learn and adapt. If the interviewer asks a question or points out a potential issue, address it thoughtfully and explain how you would modify your code to resolve it.

Meta's Official Resources

Meta provides a range of resources that are helpful for preparing. Be sure to take time to visit these sites and hear what they’re looking for directly from them.

  • Meta Careers Website, Meta Newsroom, and Meta Careers Blog: These websites offer broader insights into Meta as a company, their culture, values, and employee benefits. While not directly coding-interview focused, understanding Meta's mission and values can be helpful for behavioral interviews and demonstrating genuine interest.
  • Interviewing at Meta: The keys to success blog: This blog post, specifically released by Meta, likely contains valuable high-level advice and perspectives on the interview process. It's worth seeking out and reviewing for general guidance.

  • Meta's Technical Environment links:

    • Engineering at Meta: Provides updates and insights into Meta's engineering work and culture.
    • Meta’s Open Source website: Highlights Meta's open-source contributions, demonstrating their technical breadth.
    • Engineering Leadership at Meta blog: Offers perspectives from Meta's engineering leadership, potentially providing insights into their values and priorities. Exploring these resources can give you a better feel for Meta's technical landscape and engineering culture.

Conclusion

Landing a software engineering role at Meta is a challenging yet attainable goal. Success hinges on a combination of factors: rigorous preparation, mastery of fundamental concepts, effective practice, and confident in-interview performance.

The key takeaways for acing your Meta coding interview are:

  • Prioritize Fundamentals: Solidify your understanding of core data structures, algorithms, and time/space complexity analysis.
  • Practice Strategically: Create a structured study plan, solve problems under time constraints, and always practice thinking aloud.
  • Communicate Clearly: Remember that Meta interviews are conversations. Articulate your thought process, ask clarifying questions, explain your code, and be receptive to feedback.
  • Prepare for the Full Loop: Dedicate time to preparing for Design and Behavioral interviews; don’t spend all your time on the technical ones.

Thorough preparation significantly increases your chances of success. The Meta coding interview process is designed to be challenging, but it's also designed to identify and reward talented engineers who are well-prepared, possess strong fundamentals, and can think clearly under pressure.

So, take a deep breath, start your preparation diligently, follow the strategies outlined in this guide, and believe in your abilities. Go ace that Meta coding interview! Good luck!

. . .