Back to other articles 
Last updated: August 30, 2026 
Interviews
Machine learning engineer
Deeply researched
Machine learning engineer interviews at Meta are challenging. The questions are difficult, specific to Meta, and cover a wide range of topics.
To be a strong Meta MLE candidate, you need to demonstrate top-notch coding and ML system design skills, as well as an excellent understanding of the company’s products. And with the new AI-assisted round, you also need to show your ability to work with AI tools effectively and your great engineering judgment.
The good news is that the right preparation can make a big difference and help you land an ML job at Meta. We have put together the ultimate guide below to help you maximize your chances of success.
Here's an overview of what we'll cover:
Click here to practice 1-on-1 with ML ex-interviewers.
Key takeaways from this Meta machine learning engineer interview guide:
- You can expect an AI-assisted coding round in addition to the standard coding round in your Meta MLE interview process.
- During the AI-assisted coding round, using AI tools is optional, meaning you won’t be evaluated on how much you use them. What you'll be tested on is your critical thinking skills and your judgment as an engineer.
- In your ML system design interview, you’ll be evaluated on your ability to solve problems by building useful and scalable ML systems from scratch.
- Meta typically gives you up to three attempts to match with a team and accept an offer.
Let’s get started!
1. Meta machine learning engineer role and salary ↑
Before we go into your Meta machine learning engineer interview process and questions, let’s take a look at the role first. Alternatively, feel free to skip straight to the sections on the interview process or interview questions.
1.1 What does a Meta machine learning engineer do?
A machine learning engineer at Meta uses machine learning and artificial intelligence advancements to solve Meta’s engineering problems across its diverse ecosystem of products, including its social media apps (Facebook, Instagram, Threads, Workplace), messaging (WhatsApp), AR/VR (Reality Labs), and others.
In this role, your day-to-day will include writing and optimizing code for training and deploying AI/ML models and designing solutions for integrating AI/ML models into products or services.
Engin, ex-engineering manager at Meta, says: “One aspect of the Meta engineering culture is that engineers are expected to influence a great deal what they want to work on and how they want to hold themselves accountable.”
This means you’ll have the opportunity to focus on projects you’re passionate about, giving you more control over your professional growth and impact.
If you search for ‘machine learning engineer’ positions at Meta Careers, you’ll find most of them titled “software engineer, machine learning”. There is another machine learning engineering position called “machine learning systems engineer”. The ML systems engineer is responsible for building the infrastructure supporting the development, deployment, and scaling of ML models and applications.
What skills are required for a Meta machine learning engineer?
The minimum educational requirement for a Meta ML engineer is a bachelor’s degree in software engineering, computer science, or any related tech field.
Most posts will require 3 to 6 years of relevant engineering experience, in particular a focus on machine learning, recommendation systems, pattern recognition, NLP, data mining, or AI. If you’re applying for an ML systems engineer post, you’ll need at least 5 years of experience in a similar role.
You’ll stand out if you have prior experience developing machine learning models at scale through the entire life cycle and if you’ve had technical leadership experience.
Finally, you need to have excellent problem-solving and communication skills to stand out as a Meta ML engineer candidate.
Different job postings will have even more unique requirements, so it’s important to read through each to find a position that matches your background and interests.
1.2 How much does a Meta machine learning engineer make?
Based on Glassdoor data, the average total pay for a Machine Learning Engineer at Meta is $313k per year, about 47% higher than the US national average of $164k for the role.
Here’s a summary of the base pay and total compensation for Meta ML engineers based on August 2026 Levels.fyi data:
The average salary of Meta ML engineers also varies depending on location. For example, a Meta ML engineer at the E4 level in the U.S. typically earns significantly more than an E4 ML engineer based in India.
We presume you already know which level you’re applying for. Still, it’s good to check with your recruiter. They’ll be able to advise you which level you’re being evaluated for.
Ultimately, your interview performance will help determine how much you’re offered. That’s why hiring one of our ex-Meta interview coaches can provide such a significant return on investment.
And remember, compensation packages are always negotiable, even at Meta. So, if you do get an offer, don’t be afraid to ask for more. If you need help negotiating, get tips from our Meta salary negotiation guide and consider booking one of our salary negotiation coaches for expert advice.
Coaches who contributed to this guide
2. Meta machine learning engineer interview process and timeline↑
The process outlined below is a general overview of the Meta MLE interview process for all levels. If you're interviewing for a senior individual contributor (IC) role, it might change slightly. Check out our guides to Meta E5 and Meta E6 interviews for more details.
2.1 What interviews to expect
What's the Meta interview process and timeline for a machine learning engineer role? It normally follows the steps below:
Let's dig into each of these steps in more detail.
2.1.1 Resume screen
First, recruiters will look at your resume and assess if your experience matches the open position. This is the most competitive step in the process, as millions of candidates do not make it past this stage.
So take extra care to tailor your resume to the specific position you're applying for. Read our guide on machine learning resumes or best practices and see real Meta resume examples to learn what stands out.
If you’re looking for expert feedback, get input from our team of ex-FAANG recruiters, who will cover what achievements to focus on (or ignore), how to fine-tune your bullet points, and more.
2.1.2 Recruiter screen
In most cases, you'll start your interview process with Meta by talking to an HR recruiter. They are looking to confirm that you've got a chance of getting the job at all, so be prepared to explain your background and why you’re a good fit at Meta.
You should expect typical behavioral and resume questions like "Tell me about yourself", "Why Meta?", or "Tell me about your current project."
If you get past this first HR screen, the recruiter will then help schedule your next interview (a tech screen).
One great thing about Meta is that they are very transparent about their recruiting process. As a result, your HR contact will probably walk you through the remaining steps in the hiring process at this time. They will most likely advise you to review these official interview guides for the initial tech screen and final loop.
2.1.3 Initial tech screen
The initial tech screen is a 45-minute interview with a Meta engineer. It’s designed to assess your technical skills.
Once you’re scheduled for this tech screen, be sure to let your recruiter know which coding language you’ll be using.
The session is divided into 3 parts:
-
Introduction, during which you’ll be asked about yourself and your background. Prepare a succinct description of yourself, your professional experience, expertise, and interests. Keep it under 5 minutes.
-
Coding, which is the bulk of the interview, at 35 minutes. Expect to answer 2 coding problems focused on CS fundamentals like algorithms, data structures, recursions, and binary trees. If your tech screen is in person, prepare to whiteboard. If it’s through video or phone, prepare to code on an editor like Coderpad.io. Make sure to clarify this with your recruiter when you get scheduled for a tech screen.
-
Questions, during which the interviewer will give you 5 minutes to ask some questions about the job.
In addition to this session, you may also be asked to complete an additional CodeSignal test.
When prepping for this part of the application process, set a timer to solve medium and hard questions in 15 minutes or less (as you’ll be solving 2 questions in 35 minutes), and practice talking your way through your process.
2.1.4 Full interview loop
Once you pass the initial tech screen, your recruiter will schedule you for the full interview loop. This is the real test. You'll typically do up to six different interviews on a set of topics, and you should plan to spend the full day interviewing.
Each interview will last about 45 minutes and will focus on one of the following topics:
- Coding interview, where you'll solve algorithm and data structure questions similar to those you'd encounter in a software engineer interview.
- AI-assisted coding interview, where you’ll be allowed to use AI tools to work through a multi-part problem and be evaluated on your understanding of existing code and ability to build new functionality and extend a system.
- Machine learning system design, where you’ll be asked to design ML systems to benefit a particular feature/solve a problem
- Behavioral interview, where you can expect questions about your background, accomplishments, and your motivation for applying to Meta
During the full interview loop, you'll typically get two coding interviews (one standard and one AI-assisted), one or two ML system design interviews, and one behavioral interview. The exact breakdown may vary depending on the role, team, and level you're applying for.
It's worth briefly mentioning that some candidates have reported a different interview process, where they move from coding interviews straight to team matching interviews (which are similar to behavioral interviews).
However, we expect that process may apply only in unique circumstances. So, you should prepare for the interview process outlined above, unless you’ve been informed otherwise by Meta.
2.2 What happens behind the scenes
Throughout the interview process at Meta, the recruiter usually plays the role of "facilitator" and moves the process from one stage to the next. Here's an overview of what typically happens behind the scenes:
- After the initial tech screen, the interviewer you've talked to will have 24 hours to submit their ratings and notes to the internal system. Your recruiter then reviews the feedback and decides to move you to the full interview loop or not, depending on how well you've done.
- After the interview loop, your interviewers will make a recommendation on hiring you or not, and the recruiter compiles your "packet" (interview feedback, resume, referrals, etc.). If they think you can get the job, they will present your case at the next candidate review meeting.
- Candidate review meetings are used to assess all candidates who have recently finished their interview loops and are close to getting an offer. Your packet will be analyzed, and possible concerns will be discussed. Your interviewers are invited to join your candidate review meeting, but will usually only attend if there's a strong disagreement in the grades you received (e.g., 2 no-hires, 2 hires). If, after discussion, the team still can't agree whether you should get an offer or not, you might be asked to do a follow-up interview to settle the debate. At the end of the candidate review meeting, a hire / no-hire recommendation is made for consideration by the hiring committee.
- The hiring committee includes senior leaders from across Meta. This step is usually a formality, and the committee follows the recommendation of the candidate review meeting. The main focus is on fine-tuning the exact level and, therefore, the compensation you will be offered.
You'll get into the team matching phase at the hiring committee stage. This is where you’ll speak with hiring managers to assess mutual fit based on your interests and their team’s needs. Learn more about the Meta team matching process here.
Note that hiring managers and people who refer you have little influence on the overall process. They can help you get an interview at the beginning, but that's about it.
3. Meta machine learning engineer example questions↑
Okay, now that we've covered the interview process, let's dig into the three types of interviews that you'll encounter:
Below, we've put together a summarized list of example questions for each of these interview types. Note that we've edited the language in some places to improve the clarity or grammar of the questions. We've also provided a few notes about them to help you get a better idea of what to expect.
Let's jump in!
3.1 Coding interview↑
You'll have coding interviews during your initial tech screen and full loop as a Meta MLE candidate.
Meta's engineers (across disciplines) use code to solve difficult problems the company faces. As a result, it's essential for its new engineers to have strong foundational coding skills.
Since you're applying for an ML position, you're probably wondering what kind of machine learning problems you'll be asked. We'll get into that more in the section below. But for coding interviews, you can expect data structure and algorithm questions that are very similar to the questions a normal Meta software engineer candidate would be asked.
So, to help you structure your preparation, we've gathered the most common coding question categories asked at Meta below. In some cases, we've modified the phrasing to match the closest problem on LeetCode or another resource, and we've linked to a free solution. This is based on our analysis of Glassdoor data for Meta software engineer interviews.
Example coding questions asked in Meta MLE interviews
1. Arrays / Strings (38% of questions, most frequent)
- Given an array
numsof n integers where n > 1, return an arrayoutputsuch thatoutput[i]is equal to the product of all the elements ofnumsexceptnums[i]. (Solution) - Given a non-empty string
s, you may delete at most one character. Judge whether you can make it a palindrome. (Solution) - Implement
next permutation, which rearranges numbers into the lexicographically next greater permutation of numbers. (Solution) - Given a string S and a string T, find the minimum window in S which will contain all the characters in T in complexity O(n). (Solution)
- Given an array of strings strs, group the anagrams together. (Solution)
- Given a string s containing just the characters '(', ')', '{', '}', '[' and ']', determine if the input string is valid. (Solution)
- Given an array
numsof n integers, are there elements a, b, c innumssuch that a + b + c = 0? Find all unique triplets in the array which gives the sum of zero. (Solution) - Compare two texts and return the top k common characters. (Solution)
- Find the maximum in an array. If multiple elements share the same maximum value, return one of them uniformly at random. (Solution)
- Return the output as a concatenation of words from the dictionary separated by a single space. (Solution)
Example: string = "catsanddogs", wordDict = ["cat", "sand", "cats", "dog", "and"].
Possible outputs: "cat sand dogs" or "cats and dogs". - Return the top k integers with the highest frequencies in an array. (Solution)
- Find the length of the longest subarray whose sum equals a given target. (Solution)
Check out our list of array interview questions and string interview questions for more samples with solutions.
2. Graphs / Trees (29%)
- Given the
rootnode of a binary search tree, return the sum of values of all nodes with value betweenLandR(inclusive). (Solution) - Given a Binary Tree, convert it to a Circular Doubly Linked List (In-Place). (Solution)
- Implement an iterator over a binary search tree (BST). Your iterator will be initialized with the root node of a BST. (Solution)
- Given a binary tree, you need to compute the length of the diameter of the tree. (Solution)
- Serialize and deserialize a binary tree. (Solution)
- Given a binary tree, find the maximum path sum. (Solution)
- Given a sorted dictionary (array of words) of an alien language, find order of characters in the language. (Solution)
-
Check whether a given graph is Bipartite or not. (Solution)
- Convert a binary tree into a doubly linked list using pre-order traversal. (Solution)
- Find the maximum subtree sum. Consider every node in the tree as a potential root, calculate the sum of its subtree, and return the maximum. (Solution)
- Compute the height of a tree where each node can have an odd number of children only. (Solution)
Check out our list of graph interview questions and tree interview questions for more samples with solutions.
3. Dynamic Programming (18%)
- Given a list of non-negative numbers and a target integer k, write a function to check if the array has a continuous subarray of size at least 2 that sums up to the multiple of k, that is, sums up to n*k where n is also an integer. (Solution)
- Say you have an array for which the ith element is the price of a given stock on day i. If you were only permitted to complete at most one transaction (i.e., buy one and sell one share of the stock), design an algorithm to find the maximum profit. (Solution)
- Given an input string (
s) and a pattern (p), implement regular expression matching with support for'.'and'*'. (Solution) - You are given a list of non-negative integers, a1, a2, ..., an, and a target, S. Now you have 2 symbols
+and-. For each integer, you should choose one from+and-as its new symbol. Find out how many ways to assign symbols to make sum of integers equal to target S. (Solution)
Check out our list of dynamic programming interview questions for more samples with solutions.
4. Search / Sort (9%)
- We have a list of
pointson the plane. Find theKclosest points to the origin(0, 0). (Solution) - Given two arrays, write a function to compute their intersection. (Solution)
- Given an array of meeting time intervals consisting of start and end times [[s1,e1],[s2,e2],...] find the minimum number of conference rooms required. (Solution)
Check out our search interview questions and sort interview questions for more samples with solutions.
5. Linked lists (4%)
- A linked list is given such that each node contains an additional random pointer which could point to any node in the list or null. Return a deep copy of the list. (Solution)
Check out our list of linked list interview questions for more samples with solutions.
6. Stacks / Queues (2%)
- Implement the following operations of a queue using stacks. (Solution)
Check out our list of stack interview questions and queue interview questions for more samples with solutions.
Finally, we recommend reading our guide on Meta coding interviews, how to answer coding interview questions, and practicing with this list of coding interview examples in addition to those listed above.
3.2 AI-assisted coding interview↑
One crucial update to the Meta SWE/MLE full interview loop is the AI-assisted coding round. It’s still in its pilot phase, but many recent MLE candidates have reported getting one.
A useful way to frame the use of AI in coding is to look at it as a tool best used for local optimization (i.e., syntax, structure, enumeration, summarization). The candidate remains responsible for:
- Problem decomposition
- Architecture choices
- Correctness
- Tradeoffs
- Communication
That ownership is exactly what interviewers evaluate in this round.
To help you prepare for this new type of coding interview, here’s a breakdown of how the round works and how you can maximize the use of AI to ace the interview, according to John (Meta engineering manager).
A quick note about our expert resource for this section: John is an engineering manager who has interviewed 3000+ candidates across software engineering and management roles at Meta and other top tech companies like AWS and Huawei.
Meta's AI-assisted coding round
Interview format
You will work through one multi-part problem over the course of about 60 minutes.
The question is structured into several stages, allowing the interviewer to observe how you understand existing code, build new functionality, and extend a system.
The round allows you to use AI assistance in CoderPad, but using it is optional, meaning you are not evaluated on how often you use it. What matters is your ability to think critically, understand the code, and make informed decisions.
If you use AI-generated suggestions, be ready to explain:
- why the code works,
- what trade-offs were made, and
- how it fits into the larger system.
Environment
When you start this round, your interviewer will likely give you an overview of the coding environment. This is what it will generally include:
• Pre-written code and test cases
Some helper code, data files, or tests may already be provided so you can focus on reasoning and problem-solving rather than boilerplate.
• Moderate codebase size
Expect around a few hundred lines of existing code or data. This is intended to provide context and prevent superficial solutions.
• Both coding and non-coding tasks
Not every step involves writing code. You may be asked to:
- analyze runtime behavior,
- interpret data,
- justify design choices,
- reason about contracts or interface changes.
Typical structure
You’ll find that the structure of this coding round will be in three parts:
Part 1: Explore and Fix Issues
Begin by reading through the existing files, tests, and data to get a clear sense of how the system works.
Then start examining the existing code and resolving failing tests or small bugs. This should help you warm up and become familiar with the project before moving into larger tasks.
Part 2: Implement New Functionality
Next, you’ll be asked to complete a clear, well-defined requirement, like a new function or feature.
For this part, you may implement this yourself or use AI assistance, but you should still be able to understand and justify the final result.
Part 3: Extend and Improve the System
Finally, you’ll need to tackle a broader, open-ended task which may involve:
- modifying existing components,
- adding a more complex feature,
- optimizing performance, or
- adapting the design to new conditions.
There is room for deeper exploration, so to be a strong candidate, you can and should continue advancing the solution.
Use cases
To be a strong candidate, you’ll want to use AI in the following ways:
- As a pair programmer for micro-tasks, while keeping ownership of the overall solution, tradeoffs, and verification
- As a product partner, to dig into the requirements and verify assumptions
- As a tech lead, to analyze the feasibility of the solution
Our Meta coding interview guide includes a deep dive into the AI-enabled coding round. It includes sample prompts for different use cases, how to prepare, and interview best practices, so be sure to check that out.
You may also want to check out our general guide to AI-assisted coding interviews for additional insights and tips from FAANG experts on this interview format.
3.3 Machine learning system design interview↑
Meta’s machine learning system design interviews are designed to assess whether you can successfully design and build scalable, reliable systems that deliver real business value and solve some of Meta’s problems. It’s a 45-minute stand-alone session, and you can expect to undergo one or two.
During this part of the interview loop, you’ll need to build a technically sound solution while weighing factors such as cost, latency, interpretability, user experience, and more. You’ll be assessed on your skills in problem navigation, training data, feature engineering, modeling, and evaluation & deployment.
In “standard” system design interviews, you focus mainly on designing distributed systems and managing infrastructure components. Both follow similar principles, but ML system design goes a step further by integrating data pipelines, model training, and serving into the overall system.
The examples below are the three provided by Meta in their ML engineer full interview loop prep guide. Many recent candidates on Glassdoor report getting these exact questions for the ML system design interview.
Machine learning system design questions asked at Meta MLE interviews
- Design a personalized news ranking system.
- Design a product recommendation system (Solution)
- Design an evaluation framework for ads ranking.
- Design the “next post” logic for Facebook’s news feed with given constraints and optimization goals.
- Design a recommendation system for Facebook Ads, explaining your process and justifying your choices step by step.
- Design a model for language translation.
- Design a Story feature for Instagram and describe the ML components involved.
- Design a machine learning app that recommends places for users to visit along their trip.
- Design an end-to-end classification pipeline for Facebook Marketplace.
Check out our machine learning system design interview guide for a sample answer outline and more practice questions.
3.4 Behavioral interview↑
At any point during the interview process, you’ll encounter behavioral or "resume" questions. These questions focus on your past work experience, your qualifications, and your motivation for applying to Meta. In other words, it's a way for your interviewer to get to know you better.
Behavioral questions are a great opportunity to tell your story, highlight your qualifications, and demonstrate your alignment with Meta's values and culture.
You should also be ready to drill down into the technical details of some projects on your resume and discuss what you'd like to work on. Having clarity in these areas will help you make a strong impression.
Pranav, ex-Meta engineering manager, told us that Meta looks for the following key behavioral traits in engineers:
- How good are you at resolving conflict with your peers, managers, or external teams?
- Do you have a growth mindset? Do you accept feedback openly and work to resolve it?
- How well do you deal with ambiguity? How do you bring structure in place when there is none?
- How do you sustain progress despite multiple hurdles?
- How well are you at communicating with peers, cross-functional partners, and the team?
Another thing that makes Meta unique is its bottom-up culture, says Tom, ex-data engineering manager at Meta. For every Meta candidate, especially those in the engineering roles, "There is an expectation that you will have a strong level of autonomy, ownership, and dealing with ambiguity." Come prepared with stories of how you worked through ambiguity and how you demonstrated ownership in your previous roles.
Below, we've listed several example behavioral questions that were asked in either Meta machine learning engineer or Meta software engineer interviews, according to data from Glassdoor.
Behavioral interview questions asked at Meta MLE interviews
- Tell me about yourself.
- Why Meta?
- Give me an example of a project where you used data and machine learning.
- Tell me about a time you faced an obstacle and how did you resolve it?
- Tell me about a recent / favorite project and some of the difficulties you had.
- Tell me about the greatest accomplishment of your career.
- Tell me about a time you struggled to work with one of your colleagues.
- Tell me about a time you had to resolve a conflict in a team.
- Tell me about a time you were given feedback that was constructive.
- Tell me about a time you had to step up and take responsibility for others.
- Tell me about your worst boss and why they were bad.
- Tell me about a time you maintained an end-to-end ML pipeline in production? Describe your role and what you learned from it.
- What is your proudest project, and why?
To practice with more questions and learn a repeatable framework to structure your stories, check out our Meta behavioral interview guide.
4. Meta machine learning engineer interviewing tips ↑
You might be a fantastic ML engineer, but unfortunately, that won’t necessarily be enough to ace your interviews at Meta. Interviewing is a skill in itself that you need to learn.
Let’s look at some key tips to make sure you approach your interviews in the right way.
4.1 Ask clarifying questions
Often, the questions you’ll be asked will be quite ambiguous, so make sure you ask questions that can help you clarify and understand the problem. Most of the questions will focus on testing your technical proficiency.
4.2 Communicate efficiently
During your technical and behavioral interviews, you’ll also be assessed on your communication skills and how you talk through your problem-solving process and experience. Use this as your opportunity to show how well you communicate and collaborate with colleagues by treating your interviews like a conversation.
A great way to improve this skill is to familiarize yourself with answer frameworks. Here are our recommendations: the 4-step coding answer framework, IGotAnOffer’s SPSIL framework (Situation, Problem, Solution, Impact, Learning) for behavioral interviews, and the 4-step ML system design framework.
4.3 Scope the problem and state your assumptions clearly
During your machine learning system design interview, you will likely be asked to design large-scale systems like Spotify or YouTube. Since this can be impossible in around 45 minutes, it’s important to scope out the problem first.
Start by clarifying the requirements with your interviewer. Then, clearly state your assumptions and check with your interviewer to see if those assumptions are reasonable.
For example, during a system design interview, Oussama, ex-Amazon ML Solutions Architect, says to “ask your interviewers which part of your architecture they want you to detail or which assumptions you would like to make.”
Oussama says that doing so will help reduce the scope and clarify the context of the problem. “Feel free to make assumptions as long as you communicate them clearly. Share your thought process, including both the choices you make and the ones you discard.”
4.4 Present multiple possible solutions
When you code, present multiple possible solutions if you can. Meta wants to know your reasoning for choosing a certain solution.
4.5 Brute force, then iterate
When solving a coding problem or designing a system, don’t necessarily go for the perfect solution straight away. Meta recommends that you first try and find a solution that works, then iterate to refine your answer.
4.6 Keep your code organized and simple
Make sure to keep your code organized so your interviewer won’t have a hard time understanding what you’ve written. Meta wants to see that your code has captured the right logical structure.
Keep your code simple. If you can’t explain it in 5 minutes, it might be too complex.
4.7 Get comfortable with coding on various mediums and with AI tools
Meta typically asks interviewees to code in CoderPad.io if the interview is done online. But if the interview is in person, you might be asked to code on paper or a whiteboard. You might also get an AI-assisted round.
Check with your recruiter which one it will be if you’re not sure which medium to use, and then incorporate the appropriate tools in your prep.
4.8 (AI-assisted round) Use AI only as a tool, not as a full solution generator
The biggest mistake candidates make during the AI-enabled coding round, according to John (Meta EM), is using AI as a full solution generator. This is a red flag. AI assistance is there as a tool, and your goal as an engineer is to drive the quality of your solution.
4.9 Be honest
Meta does not expect its candidates to know everything, so if you get a question that’s outside of your expertise, don’t hesitate to let your interviewer know. Ask the right questions and show motivation to learn.
Similarly, when asked if you faced challenges or setbacks, don’t deny or frame your weakness as a strength. Instead, discuss how you’ve improved and learned from these challenges.
4.10 Center on Meta’s values
Make sure to review Meta’s core values and align your behavioral responses with them. Prepare anecdotes/concrete examples from your professional experience corresponding to each Meta core value. To be more efficient, adapt your stories so they can respond to different values.
5. Preparation plan ↑
Now that you know what questions to expect, let's focus on how to prepare. It's no secret that the performance bar at Meta is quite high. To help you maximize your chances of landing an offer, we've listed the four steps we recommend taking to prepare below.
5.1 Learn about Meta's culture
Most candidates fail to do this. But before investing a ton of time preparing for an interview at Meta, you should make sure it's actually the right company for you.
Meta is prestigious, and so it's tempting to assume that you should apply without considering things more carefully. But it's important to remember that the prestige of a job (by itself) won't make you happy in your day-to-day work. It's the type of work and the people you work with that will.
If you know any engineers, data scientists, or other professionals who work at Meta (or used to), it's a good idea to talk to them to understand what the culture is like.
In addition, we would recommend checking out these resources:
- Meta's 6 core values
- Facebook’s hacker culture (by Mark Zuckerberg, via Wired)
- Meta annual reports and strategy presentations (by Meta)
- Meta's approach to tech trends (by CB Insights)
- Meta org culture analysis (by Panmore Institute)
- ML Applications Archive (by Meta)
- ML Research (by Meta)
5.2 Practice by yourself
As mentioned above, you'll have three main types of interviews for the ML engineering position at Meta: coding, machine learning system design, and behavioral.
As we've outlined above, you'll have to prepare for several different categories of questions ahead of your Meta MLE interviews.
As a starting point, be sure to read Meta’s official MLE guides to the initial tech screen and final loop.
In this article, we've recommended various deep-dive articles that will help you prepare for each question category. Here's the complete list, plus a couple extras we think could be useful. Each guide contains several practice questions, plus answer frameworks and tips from experts.
- Standard and AI-assisted coding: Meta coding interview, AI-assisted coding interview
- ML system design: ML system design interview
- Standard system design and product design: Meta system design interview, Meta product architecture interview
- Behavioral: "Why Meta?" interview question, Meta behavioral interview questions
- Project retrospective (for Meta MLE E5 above): Meta project retrospective interviews
- Meta interview experiences
If you’re applying for MLE / AI roles at FAANG+/AI labs, we recommend reading our other related interview guides:
- Google machine learning engineer interview
- Amazon machine learning engineer interview
- Apple machine learning engineer interview
- TikTok machine learning engineer interview
- OpenAI software engineer interview
- NVIDIA software engineer interview
- Anthropic software engineer interview
- AI engineer interview
Once you’ve brushed up on the different topics and familiarized yourself with the questions, practice by interviewing yourself out loud. But by yourself, you can’t simulate thinking on your feet or the pressure of performing in front of a stranger. Plus, there are no unexpected follow-up questions and no feedback.
That’s why many candidates try to practice with friends or peers.
5.3 Practice with peers
If you have friends or peers who can do mock interviews with you, that's an option worth trying. It’s free, but be warned, you may come up against the following problems:
- It’s hard to know if the feedback you get is accurate
- They’re unlikely to have insider knowledge of interviews at your target company
- On peer platforms, people often waste your time by not showing up
For those reasons, many candidates skip peer mock interviews and go straight to mock interviews with an expert.
5.4 Practice with experienced Meta interviewers
In our experience, practicing real interviews with experts who can give you company-specific feedback makes a huge difference.
For the new AI round in particular, practicing with an expert is crucial, according to John (Meta EM). This round is as much about process as outcome, and is a combination of system design, data structures, algorithms, reasoning, and mimicking real production problems. Live coaching can help you:
- Calibrate your AI usage
- Improve your technical communication
- Structure your problem-solving logic
- Develop structured debugging habits
Find a Meta machine learning interview coach so you can:
- Test yourself under real interview conditions
- Get accurate feedback from a real expert
- Build your confidence
- Get company-specific insights
- Learn how to tell the right stories better
- Save time by focusing your preparation
Landing a job at a big tech company often results in a $50,000 per year or more increase in total compensation. In our experience, three or four coaching sessions worth ~$500 make a significant difference in your ability to land the job. That’s an ROI of 100x!
ML system design
