Meta Machine Learning Interview Questions
We present a curated collection of data science and machine learning
interview questions that are commonly asked at Meta. These questions
span a range of categories including SWE coding, machine learning
coding, machine learning system design, machine learning theory and
behavioral aspects. This comprehensive list is designed to provide a
thorough overview of the types of challenges and inquiries that
candidates might encounter during their interview process with Meta.
Coding Questions
This interview tests a wide array of programming skills, from basic
string and array manipulation to complex data structures such as trees
and graphs. Your goal is to demonstrate strong problem-solving skills
and coding proficiency.
- Given a list of numbers, find all pairs that sum up to a specific
target.
- Find the first non-repeated character in a string.
- Given an array of integer intervals (from ai to bi), determine the
value of x such that x is within the maximum number of intervals.
- For an array, find the longest increasing subsequence considering
various constraints.
- Given an array of integer intervals (from a_i to b_i), determine the
value of x such that x falls within the maximum number of
intervals.
- Implement a function to find anagrams within a list of words.
- Given a custom order of the characters in the alphabet, how would
you sort strings according to this new order?
- Determine if one string is a rotation of another string.
- Find the smallest window in a string that contains all characters of
another string.
- Check if a string is a valid IP address.
- Construct a heap data structure from an arbitrary list of
numbers.
- Find the lowest common ancestor (LCA) of two nodes in a binary
tree?
- Write a function to invert a binary tree.
Machine Learning Theory
The topics in this interview not only cover fundamental statistical
theories and models but also extend to advanced deep learning algorithms
which are most commonly used now. As you prepare for interviews, these
questions will help you identify the topics which are important to
understand both from theory and applications perspective.
- Explain the bias-variance tradeoff and its significance.
- Can you explain linear regression and its underlying
assumptions?
- How does logistic regression work, and what is its connection to
maximum likelihood estimation?
- What is regularization, and why is it important? Compare L1 and L2
regularization.
- Explain the difference between bagging and boosting.
- How do Random Forests and Gradient Boosting Machines (GBMs)
differ?
- What is PCA (Principal Component Analysis), and when would you use
it?
- Explain k-means clustering and its limitations.
- How do you evaluate model performance? Compare precision, recall, F1
score, and AUC-ROC.
- What is the vanishing gradient problem, and how is it
addressed?
- Explain the purpose of dropout in neural networks.
- How does backpropagation work in training a neural network?
- What is batch normalization, and how does it improve training in
deep neural networks?
- How do residual networks (ResNets) work, and why are skip
connections important?
- What is the purpose of weight initialization, and how does it affect
model convergence?
- How do gradient clipping and gradient scaling address exploding
gradient problems in deep neural networks?
- What are attention mechanisms in deep learning, and how do they
improve model performance?
- What are the main differences between instance normalization, layer
normalization, and group normalization?
- How do multi-head attention mechanisms work, and why are they crucial in transformer models like BERT and GPT?
Machine Learning System
Design
This session is dedicated to assess your ability to design complex
machine learning systems. You will be provided an open-ended machine
learning problem. Your task is to discuss with the interviewer about
various design choices and assumptions to convert this into a concrete
system. This involves covering various aspects of a ML system such as
data, modeling, evaluation, continuous improvements, deployment and
monitoring.
- Design a recommendation system for Facebook News Feed.
- Develop a model to detect and filter out friend requests on facebook
which are likely spam.
- Design a real-time content moderation system to detect and remove
inappropriate content (e.g., hate speech, violence).
- Design a spam detection system for Facebook Messenger.
- Design a system to recommend friends on Facebook (People You May
Know feature).
- Design a system to automatically generate image captions for
visually impaired users on Facebook.
- Design a system to generate personalized text content for Facebook
users, such as status suggestions or comment replies, using large
language models.
- Design a system that generates personalized avatars, stickers, and
emojis for Meta products.
- How would you design a content generation system for creating ads automatically based on business inputs using generative models?
Machine Learning Coding
Interview
Distinct from the general coding interview, this session focuses on
your ability to implement machine learning algorithms from first
principles. Generally you are only allowed to use numpy or basic matrix
manipulation libraries during these interviews.
- Write a function to implement gradient descent with parameters for
learning rate, iterations, and initial values.
- Implement the k-nearest neighbors algorithm to classify new data
points, with parameters for the number of neighbors and distance
metric.
- Write a logistic regression model from scratch, explaining the
logistic function and how to estimate coefficients using gradient
descent.
- Create a simple neural network using NumPy that includes one hidden
layer, sigmoid activations, and both forward and backward
propagation.
- Implement L1 and L2 regularization techniques in a linear regression
model and discuss their impact on performance.
- Write a function to perform stratified sampling on a large dataset,
ensuring the sample reflects the distribution of a target
variable.
- Implement functions to calculate precision, recall, and F-score for a classification model, discussing each metric’s significance in model performance evaluation.
Databases & Analytics
If you are applying for analytics or data engineering roles, you will
likely have an interview focused on SQL and databases. The interview
usually consists of testing SQL knowledge by writing queries inspired by
real world scenarios and problems that data engineers at Meta encounter
frequently.
- How do indexing and B-trees work in databases?
- Given the table messenger_sends (with columns: date, ts, sender_id,
receiver_id, message_id, and has_reaction), how many unique conversation
threads are present?
- Given a table with user_id and the dates they visited a platform,
how would you identify the top 100 users with the longest consecutive
streak of visiting the platform as of the previous day?
- Given a table with columns: time, post_id, action, and content
(where action can be “reported” and content is marked as spam), and
another table with time, post_id, user for all posts removed manually,
how would you determine the percentage of the previous day’s content
views that were reported for spam and removed on the same day?
- Given a table containing: date, user_id, status_id, action (which
can be like, comment, view, report), and extra (with tags like absurd,
violent), how would you calculate the number of distinct reports from
the previous day?
- Using a table of users and their login timestamps, how would you
pinpoint the initial login date for each user? How would you determine
the set of users who logged in the previous week?
- Given tables for user actions with columns: date, session_id,
user_id, event, and session details with date, session_id,
timespent_sec, user_id, how would you determine the average time spent
by each user daily?
- Given a table “friends” with columns: user_id, friend_id, and
date_added, how would you write a query to find users with the most
mutual friends?
- Given tables posts (with columns: post_id, user_id, date, content)
and likes (with columns: like_id, post_id, user_id, date), how would you
write a query to identify the top 10 most liked posts for the previous
week?
- Describe your approach to designing a model that identifies poor-performing sellers on a marketplace. What metrics would you employ to ascertain if your model is functioning as intended and to evaluate product success?
Behavioral
In the behavioral interview, you’ll be asked about your previous
experiences, how you work in teams, handle challenges, and align with
Meta’s values. This interview is typically conducted by your hiring
manager or someone at similar levels in the organization.
- Describe a time when you faced a significant challenge in a project.
How did you overcome it?
- What’s the most significant positive feedback you’ve received in
your career? Conversely, what’s the most critical feedback you’ve
encountered in your professional journey?
- Describe an instance when a deliverable or project did not go as
planned. How did you address the situation?
- Given Meta’s array of products and features, which ones do you feel
most passionate about, and how might they be enhanced?
- Describe a time when you had to navigate ambiguity in a project. How
did you handle it?
- Tell me about a time when you took ownership of a project. What
steps did you take to drive it to completion?
- Can you share an example of a situation where you learned from a
failure or mistake? What did you do differently afterward?
- Tell me about a time when you had to influence others without direct authority. How did you approach it?
