Meta ML engineer interview (questions, prep, and process)

Machine learning engineer interviews at Meta (formerly Facebook) are really challenging. The questions are difficult, specific to Meta, and cover a wide range of topics.

The good news is that the right preparation can make a big difference, and can 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:

• Process and timeline
• Example questions
  • Coding interview
  • System design interview
  • Machine learning design interview
  • Behavioral interview
• Preparation tips

Click here to practice 1-on-1 with ML ex-interviewers

1. Interview process and timeline

1.1 What interviews to expect

What's the Meta machine learning engineer interview process and timeline? It normally follows the below steps and takes four to eight weeks to complete:

1. Resume screen
2. Recruiter screen
3. Coding interview
4. Onsite interviews (five interviews)

Next, we'll dig into each of these steps in more detail. If you're interviewing with multiple companies, take a look at our guides to the Google ML engineer interview and the Amazon ML engineer interview.

1.1.1 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 to.

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.

1.1.2 Recruiter phone 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 coding interview). 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.

1.1.3 Coding interview

Next, you'll have a coding interview where you'll be asked data structure and algorithm questions. These questions tend to be quite similar to the questions you'd encounter as a Meta software engineer.

Your interviewer may start with a few behavioral questions, but most of the time will be spent on coding questions. You're usually given two problems to solve, and you can learn more about the types of questions to expect in section 2.1 of this guide. 

Logistically speaking, the coding interview is usually held through a video call using BlueJeans. And the coding exercises are done in CoderPad, which is a simple online code editor without syntax highlighting or auto-complete.

1.1.4 Onsite interviews

Onsite interviews are the real test. You'll typically do five different interviews on a variety 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:

1. Coding interview, where you'll solve algorithm and data structure questions similar to those you'd encounter in a software engineer interview.
2. System design interview, where you'll be asked to create a high-level design for a modern technology system like Instagram, Facebook Messenger, etc.
3. Machine learning design interview, where you'll need to suggest an approach for how to solve a problem using a machine learning solution.
4. Behavioral interview, where you can expect questions about your background, accomplishments, and your motivation for applying to Meta.

During the onsite, you'll typically get two coding interviews, one system design interview, one machine learning design interview, and one behavioral interview. Just keep in mind that the exact breakdown might vary depending on the role, team, and level you're applying for.

[COVID note] It's likely that your onsite interviews will be held virtually instead of in-person, given the COVID-19 pandemic. However, your recruiter should be able to provide you with the most up-to-date information on Meta's onsite interview procedures. Feel free to ask your Meta recruiter for details after you've been officially invited to participate in the onsite interviews.

Finally, 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 would only expect that process for specialized roles or unique circumstances. So, you should prepare for the interview process outlined above, unless you have been informed otherwise by Meta. 

1.2 What happens behind the scenes

2. Example questions

Okay, now that we've covered the interview process, let's dig into the four types of interviews that you'll encounter:

• Coding
• System design
• Machine learning design
• Behavioral

Below, we've put together a summarized list of example questions for each of these interview types. We've also provided a few notes about them, which should help you get a better idea of what to expect. Let's jump in!

2.1 Coding interview

Both before and during the onsite interviews, you'll have coding interviews. Meta's engineers (across disciplines) use code to solve some of the most difficult problems the company faces. As a result, it's essential for all of the company's new engineers to have strong foundational coding skills.

Since you're applying for a machine learning position, you're probably wondering what kind of machine learning problems you'll be asked. We'll get into that more in section 2.3 (i.e. the section on machine learning design interviews), but for coding interviews, you can expect data structure and algorithm questions that are very similar to the questions a normal FB software engineer candidate would be asked. 

So, to help you structure your preparation, we've pulled in the below summary of Meta's most common coding question categories. This information is based on our analysis of Glassdoor data for Meta software engineer interviews.

1. Arrays / Strings (38% of questions, most frequent)
2. Graphs / Trees (29%)
3. Dynamic Programming (18%)
4. Search / Sort (9%)
5. Linked lists (4%)
6. Stacks / Queues (2%, least frequent)

We've also listed common examples of Meta software engineer coding questions below. To make these questions easier to study, we've modified the phrasing to match the closest problem on Leetcode or another resource, and we've linked to a free solution. 

Finally, we recommend reading this guide on how to answer coding interview questions and practicing with this list of coding interview examples in addition to those listed below.

2.2 System design interview

Facebook, Instagram, and Whatsapp all have 1bn+ monthly active users. Therefore, Meta machine learning engineers need to be able to understand and work with systems that are highly scalable. The coding questions we've covered above usually have a single optimal solution. But the system design questions you'll be asked are typically more open-ended and feel more like a discussion.

This is the part of the interview where you want to show that you can both be creative and structured at the same time. Below are the most common system design questions asked in Meta software engineer interviews, according to data from Glassdoor.

As with coding interviews, the system design questions you'd face as a machine learning engineer should be similar to those asked in normal Meta software engineer interviews. As a result, we'd recommend using the below questions in your preparation. For more questions, take a look at our list of 31 system design interview questions.

2.3 Machine learning design interview

All of the interviews we've covered so far have considerable overlap with the Meta software engineer interviews. But the machine learning design interview is specific to candidates for Meta's machine learning engineer roles.

Meta has huge data sets and billions of users across their various apps. The magnitude and complexity of these systems present quite a few opportunities to apply machine learning to real-world problems. And, broadly speaking, that's what you'll be asked to do during the machine learning design interview.

The questions you'll be asked are somewhat similar to system design questions, in that you'll need to outline a high-level approach for a system or problem. But, the primary difference is that you'll be expected to specifically develop a machine learning solution. 

To provide a clearer idea of what to expect, we've compiled a list of examples that are similar to the questions you'd be asked in Meta's machine learning design interview. These questions are from three sources: a machine learning guide from Meta, Glassdoor, and from Rahul Agarwal. Note that we've modified the phrasing in some cases to make the questions more clear, and we've listed the original source after each question in parentheses for reference.

2.4 Behavioral interview

For the behavioral interview, and sometimes at the beginning of your other interviews, you'll be asked 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, to highlight your top qualifications, and to demonstrate your alignment with Meta's values and culture. You should also be ready to drill-down into the technical details of the projects on your resume, and to discuss the types of projects you'd like to work on in the future. Having clarity in these areas will help you make a strong impression in behavioral interviews.   

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. This should give you a good list of practice questions to start preparing with. Note that we've edited the language in some places to improve the clarity or grammar of the questions. 

3. How to prepare

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.

3.1 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 reading about Meta's 6 core values and Facebook's hacker culture and Meta's machine learning careers page. 

3.2 Practice by yourself

As mentioned above, you'll have four main types of interviews at Meta: coding, system design, machine learning design, and behavioral. 

For coding interviews, we recommend reading this article written by an ex-Facebook interviewer to understand more about the step-by-step approach you should use to solve coding questions in an interview.

And to practice, we recommend using our articles, 73 data structure questions and 71 algorithms questions, which have links to high quality answers to each problem. They are organized by type of data structure or algorithm as well as by difficulty level.

For system design interviews, we recommend studying our system design interview guide and learning how to answer system design interview questions. These guides cover a step-by-step method for answering system design questions, and they provide example questions with solutions. 

For machine learning design interviews, we recommend that you study Meta's machine learning field guide to make sure you understand the end-to-end process for implementing machine learning solutions. Then, you can get some practice using the example questions we've provided in section 2.3 above. 

For behavioral interviews, we recommend learning our step-by-step method for answering behavioral questions. Then, you can practice answering the questions listed in section 2.4 above. If you have more time to prepare, then you can prepare even more "stories" summarizing your top qualifications or important lessons that you've learned.

Finally, a great way to practice all of these interviews is to interview yourself out loud. This may sound strange, but it will significantly improve the way you communicate your answers during an interview. Play the role of both the candidate and the interviewer, asking questions and answering them, just like two people would in an interview. Trust us, it really helps!

3.3 Practice with peers

Practicing by yourself will only take you so far. One of the main challenges of machine learning interviews is communicating what you are doing as you are doing it. As a result, we strongly recommend practicing live interviews with a peer interviewing you.

If possible, a great place to start is to practice with friends. This can be especially helpful if your friend has experience with machine learning engineer interviews, or is at least familiar with the process.

3.4 Practice with ex-interviewers

Finally, you should also try to practice machine learning mock interviews with expert ex-interviewers, as they’ll be able to give you much more accurate feedback than friends and peers. .

If you know a machine learning engineer or someone who has experience running interviews at Meta or another big tech company, then that's fantastic. But for most of us, it's tough to find the right connections to make this happen. And it might also be difficult to practice multiple hours with that person unless you know them really well.

Here's the good news. We've already made the connections for you. We’ve created a coaching service where you can practice 1-on-1 with ex-interviewers from leading tech companies like Meta. Learn more and start scheduling sessions today.