Top 23 Meta Machine Learning Interview Questions + Guide in 2024

Meta significantly ramped up its hiring in machine learning and AI at the end of 2023. At the modern Meta, machine learning is used to improve nearly every Meta product and service, from translation algorithms that translate billions of posts per day, to computer-vision algorithms that process images and videos.

As such, Meta machine learning interview questions vary by role and team.

For example, somebody interviewing for a Meta machine learning engineer role, working on the Meta News Feed algorithm, would face recommendation engine machine learning questions. A machine learning scientist (in-house known as “data scientist, machine learning”) working on the Data Center team, however, might expect more forecasting and modeling questions.

No matter the role, Meta machine learning interviews are fairly standardized, with technical and on-site rounds focused on machine learning and AI. This guide offers insights into the interview process, an overview of key machine learning roles at Meta, as well as examples of Meta machine learning interview questions.

Meta’s machine learning operation is very sophisticated, and there are a wide range of well-defined ML roles. Here are the most common:

• Machine Learning Scientist - These are data science roles that specialize in machine learning. These roles typically blend analysis, with a focus on architecting and tuning ML models.

• Machine Learning Engineer - Meta’s ML engineers design and deploy machine learning models, while also optimizing and tuning algorithms.

• Research Scientist, Machine Learning - These roles typically require a PhD, and they’re focused on a certain area of machine learning, including AI, natural language processing, and computer-vision. These roles research and develop new models and machine learning techniques.

• Research Engineer, Machine Learning - Similar to the research scientist, these roles focus on researching new machine learning engineering solutions. Focus areas tend to include AI and computer vision.

How do interviews differ?

Every role will face a technical screening round that focuses on SQL and/or Python questions. Typically, this round features intermediate-to-advanced algorithms-style questions.

The big difference is during the on-site interview. Research scientist roles include a presentation/research discussion during the on-site. Otherwise, the interview process is fairly similar across all roles.

The structure of the interview is similar across all roles, yet the types of questions you will be asked are very dependent on the team and role.

Here’s a high-level overview of the machine learning interview process at Meta:

• Technical screen - In most cases, you will face general algorithmic coding questions, e.g. Python problems, typically at the intermediate or advanced level.

• Onsite interview - Machine learning roles will include 1-2 rounds of machine learning system design, as well as an additional 1-2 rounds of ML coding. Behavioral and research (for research scientist roles) rounds are also common.

Meta asks machine learning questions in interviews for data science, machine learning engineers and AI scientists.

In general, Meta machine learning interview questions fall into two categories:

• Algorithmic Coding Questions - These questions cover data structures and algorithms, similar to what you’d find on Leetcode. Most questions start at the medium level in the technical screen.

• Machine Learning System Design - ML system design questions explore the design and architecture of machine learning models, recommendation engines and other machine learning applications.****

• Applied Modeling - Applied modeling is a type of case question asked about practical machine learning. The most common type of question framework is: “Given an example scenario with a machine learning system or model, how would you analyze and fix the problem?”

• Recommendation Systems - This type of question is common at Meta, especially considering how News Feed is such a critical component of its most well-known product. These types of questions are similar to case studies, and they bring in ML system design elements.

One tip: Filter these questions through the product or team you’re interviewing for. Meta interviews are rather product-focused. So with every practice problem, think about which Meta products it would apply to and how scenarios would change based on those products.

Meta machine learning coding questions cover a range of topics, but in general, you can expect to focus on data structures and algorithms. Some of the most commonly covered topics include:

• Arrays and Strings
• Dynamic Programming
• Graphs and Trees
• Search and Sort
• Linked Lists

During the interview, these exercises are typically done in CoderPad, so familiarize yourself with the platform prior to your interview.

Typically, you will face 1-2 of these types of questions during the technical screen (usually medium level). Plus, you’ll likely face 1-2 rounds during the onsite that focus specifically on whiteboarding machine learning coding questions.

Here are some examples of Meta machine learning coding questions:

1. Given two strings, A and B, write a function can_shift to return whether or not A can be shifted some number of places to get B.

Example:

A = ‘abcde’

B = ‘cdeab’

can_shift(A, B) == True

This string shift problem is relatively simple if we figure out the underlying algorithm that allows us to easily check for string shifts between strings A and B. First off, we have to set baseline conditions for string shifting. Strings A and B must both be the same length and consist of the same letters.

2. Write a function get_ngrams to take in a word (string) and return a dictionary of n-grams and their frequency in the given string.

Example:

string = 'banana'

n=2

output = {'ba':1, 'an':2, 'na':2}

Hint: With this N-gram dictionary problem, remember, there are ways to divide the string into substrings where two n-grams are the same. For example, if we have the string ‘banana’, string[1:3] and string[3:5] are equal. Is there a quick way to remove these duplicates?

3. Implement Dijkstra’s shortest path algorithm for a given graph with a known source node.

Implement Dijkstra’s shortest path algorithm for a given graph with a known source node. A graph format is a dictionary of nodes as key and the value is a dictionary of neighbors to that node with the distance between them as follow.

Note: set the previous node of the source node to None.

4. How can you merge two sorted lists?

Given two sorted lists, write a function to merge them into one sorted list. For example:

list1 = [1,2,5]
list2 = [2,4,6]

The output would be:

def merge_list(list1,list2) -> [1,2,2,4,5,6]

As a bonus, can you determine the time complexity of your solution?

5. Given a string str, write a function perm_palindrome to determine whether there exists a permutation of str that is a palindrome.

Example:

str = 'carerac'

def perm_palindrome(str) -> True

Hint: “carerac” returns True since it can be rearranged to form “racecar”, which is a palindrome. With this Python permutation palindrome question, the brute force solution would be to try every permutation, and verify if it’s a palindrome.

6. Can you create a sub-$O(n)$ search algorithm for a pivoted array?

Suppose an array sorted in ascending order is rotated at some pivot unknown to you beforehand.

You are given a target value to search. If the value is in the array, then return its index; otherwise, return -1.

Notes:

• Rotating an array at pivot $n$ gives you a new array that begins with the elements after position $n$ and ends with the elements up to position $n$.
• You may assume no duplicate exists in the array.

Bonus: Your algorithm’s runtime complexity should be in the order of $O(log\;n)$.

The machine learning system design interview is typically a 45-minute standalone interview during the onsite at Meta. You may face 1-2 rounds of ML system design interviews.

Overall, these types of questions ask you to walk the interviewer through how you would design a machine learning solution for a specific business problem.

As you structure your answers, think about the standard development cycle of a machine learning solution: data collection, problem formulation, model creation, implementation of model, enhancement of models.

Plus, it’s helpful to use a framework to structure your answer like this:

1. Setting the problem statement.
2. Architecting the high-level infrastructure.
3. Explaining how data moves from one part to the next.
4. Understand how to measure the performance of the machine learning models.
5. Deal with common problems around scale, reliability, and deployment.

Here are examples of Meta machine learning system design questions to study:

7. You’re tasked with building a model to detect fraud on a banking platform.

The bank wants to implement a text messaging service in addition that will text customers when the model detects a fraudulent transaction in order for the customer to approve or deny the transaction with a text response.

Here’s an example of a first step you could take in solving this bank fraud model question:

We should summarize our findings by building out a binary classifier on an imbalanced dataset. We want to know: 1) data accuracy, 2) how the model works on an unbalanced dataset, 3) how much we care about interpretability, and 4) the costs of misclassification.

8. How would you build the recommendation algorithm for type-ahead search for Meta?

The first way many would go about solving this Meta recommendation engine problem is by thinking that, since we want a recommendation algorithm, we could set up an RNN recurrent neural network. Would there be something wrong with this approach?

9. A pizza franchise experiences a lot of no-shows after customers place an order. What features would you include in a model to try to predict a no-show?

Hint: When creating a prediction model such as in this binary classification question, it is useful to think of explanatory variables that may be important for explaining the phenomena.

This process is called manual feature selection and requires expert knowledge in the field, in this case, the running of a pizza franchise.

10. How would you design a system to automatically detect prohibited firearm listings on a marketplace?

You are designing a marketplace for a website where selling firearms is prohibited by the Terms of Service Agreement and the laws of your country. How would you create a system that can automatically detect if a listing on the marketplace is selling a gun?

11. Determine the features to extract out of Meta Reels.

Suppose that you are to redesign Meta reels. What features would you extract or utilize to create a more optimal recommendation algorithm?

During the onsite, you’ll likely face applied modeling questions, whether as a standalone session or during the system design interview. This type of question is usually a hypothetical, asking you to apply a machine learning technique to a problem.

For example, you might be asked how much data you would need to collect for highly accurate results in a binary classification model. These questions assess your ability to approach modeling problems, as well as your general knowledge of ML techniques.

Here are some sample Facebook applied modeling interview questions:

12. How would you test whether having more friends now increases the probability that a Facebook member is still an active user after 6 months?

When looking at this user probability question, we can break down our analysis into two steps: analysis of data and the use binary classification to understand feature importance.

Since we are interested in whether or not someone will be an active user in 6 months or not, we can test this assumption by first looking at the existing data. One way to do so is to put users into buckets determined by friend size 6 months ago and then look at their activity over the next 6 months.

13. How would you evaluate the effect that parents joining Facebook has on the engagement of teenage users?

With this Facebook engagement modeling question, you cannot run a randomized test, but you can perform an observational study with a quasi-experimental design. You could analyze two separate groups of teen users: Those with parents who have joined Facebook (Group A), and those with parents who have not (Group B).

You’d want to look at two separate time frames: the time before parents joined for Group A and after they joined. With this design, you could compare engagement pre- and post-change for both user groups. How would you control for bias? What variables would you watch?

14. How would you interpret coefficients of logistic regression for categorical and boolean variables?

Interpretation of coefficients in logistic regression can be complex, especially when dealing with categorical and boolean variables. How would you go about interpreting these coefficients?

15. What’s the difference between Lasso and Ridge Regression?

Lasso and Ridge Regression are two popular techniques in machine learning. Can you explain the differences between them?

16. How would you justify the complexity of a neural network model to non-technical stakeholders?

Suppose you’re asked to build a model using a neural network to solve a business problem. How would you justify the complexity of such a model and explain the predictions it makes to stakeholders who may not have a technical background?

17. What is recall? What is precision?

Recall and precision are two essential metrics used in information retrieval and classification tasks, such as document retrieval, search engines, and machine learning models. These metrics help evaluate the performance of a system or model in terms of its ability to correctly identify relevant items and minimize false positives.

1. Recall: Recall, also known as true positive rate or sensitivity, measures the ability of a system or model to identify all relevant instances within a dataset. It answers the question: “Of all the actual positive instances, how many were correctly identified?” A high recall indicates that the system is effective at capturing most of the relevant information, even if it results in some false positives.

2. Precision: Precision, on the other hand, evaluates the accuracy of the system or model in correctly identifying relevant instances among the ones it labels as positive. It answers the question: “Of all the instances labeled as positive, how many were truly relevant?” A high precision score means that the system is selective and accurate in its positive predictions, minimizing false positives.

18. How would you optimize the ratio of public versus private content in the News Feed algorithm?

With this News Feed ranking problem, you’d be provided with some sample content, e.g. popular videos, baby pictures, birthday posts, and news articles. Your goal with this type of question would be to walk through how you would build the model, highlight the features you would use, and define the metrics you would track.

19. How would you build a restaurant recommender on Facebook?

Start with how you would go about getting this data, then talk about how you would build it. Do you have any concerns about adding this feature to Facebook?

20. How would you optimize the recommendation algorithm for suggesting “People You May Know” on Facebook?

Elaborate on the techniques, data sources, and user engagement metrics you would use to improve the accuracy of friend suggestions.

21. Can you design a recommendation system for personalized content discovery in Facebook’s Marketplace?

Describe the data sources, algorithms, and user profiling methods you would employ to suggest relevant items to users based on their preferences and behavior.

22. If you were tasked with developing a music recommendation feature for Facebook, how would you approach building it?

Discuss the data sources, feature engineering, and recommendation algorithms you would use to provide personalized music recommendations to users within the Facebook platform.

23. How would you create a validation tool for Facebook Marketplace?

Let us suppose that you are working with a validation tool for Facebook Marketplace that tracks whether or not the product posting is legitimate or not. What are the possible features and approaches that you might consider?

With this type of recommendation engine practice problem, walk the interviewer through the whole framework:

1. Data Collection
2. Feature Set
3. Model Selection
4. Model Evaluation
5. Model Rollout

Prep for your Facebook ML interview with these helpful resources from Interview Query:

• Facebook Machine Learning Engineer Interview Guide

• Machine Learning and Machine Learning System Design Course