Meta Machine Learning Engineer Interview Guide

Interview Guide Dec 09

Detailed, specific guidance on the Meta Machine Learning Engineer interview process - with a breakdown of different stages and interview questions asked at each stage

The role of a Meta Machine Learning Engineer

At Meta, machine learning is used across a diverse set of applications to help people discover better content, connect with things that matter most to them, keep the community safe from harmful content, and to build the future of connection within virtual and augmented reality. As an example, Meta strives to find ways to deliver more engaging content in News Feed, present the most relevant ads possible, and to build the best HW products and tools that help people feel connected, anytime, anywhere.

The ideal candidate will have industry experience working on a range of classification and optimization problems - some examples of these include  payment fraud, click-through rate prediction, click-fraud detection, search ranking, text/sentiment classification, collaborative filtering/recommendation, or spam detection. The position will involve taking these skills/experiences and applying them to some of the most exciting and massive social data and prediction problems that exist on the web.

Meta offers several Machine Learning Engineer positions across different teams and departments. MLEs work on a wide range of projects, including personalized recommendations, content ranking, computer vision, natural language processing, and speech recognition. They collaborate closely with product teams, data scientists, and other engineers to develop and implement machine learning solutions that improve Meta's products and services. MLE positions at Meta require a strong background in computer science, mathematics, and statistics, as well as experience with programming languages like Python, Java, or C++.

It is worth noting that the available positions and locations can change frequently, so it is recommended to regularly check the career page for the latest updates.

How to Apply for a Machine Learning Engineer Job at Meta?

To apply for a Machine Learning Engineer job at Meta, you will need to visit Meta's career website and search for open Machine Learning Engineer positions. Once you have found a position that you are interested in, you will be able to submit an application online. However, we would highly recommend taking the referral route if you know someone in the company as it increases your chances meaningfully. One tip regarding your resume - make a few tweaks for the position and the role you are applying for which will help you have a better chance compared to other candidates. If you're not sure how to do that, Prepfully offers a resume review service, where actual recruiters will give you feedback on your resume.

Meta Machine Learning Engineer Interview Guide

As a part of the Meta Machine Learning Engineer interview, the candidate will need to go through multiple interview rounds:

1.  HR Interview - The recruiter will conduct a technical screening to evaluate your knowledge and experience in machine learning.

2. Technical Screen - The technical screening will be conducted over the phone with an engineer. The purpose of this round is to assess how quickly you can come up with the most efficient solution to a problem in your head and quickly code it without logic flaws.

3. Onsite Interview Rounds - The final round for the Machine Learning Engineer role at Meta consists of multiple rounds of onsite interviews.

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Meta MLE : HR Interview


The recruiter will conduct a technical screening to evaluate your knowledge and experience in machine learning. They will ask you about your areas of expertise and experience with relevant programming languages, frameworks, and tools. The recruiter will also ask you to provide examples of projects you have worked on, and how you have addressed technical challenges in those projects. This round is designed to assess your technical skills and suitability for the Machine Learning Engineer role at Meta.

Interview Questions

  • Why do you want to join Meta?
  • Why do you think you will be a good fit for the role?
  • What responsibilities do you expect to have from your job at Meta?
  • What are your areas of expertise in machine learning, and how have you developed those skills?
  • What programming languages are you comfortable with, and how have you used them in your previous projects?
  • What machine learning frameworks have you used, and how did you find them helpful in your projects?
  • Have you worked with large datasets? If so, how did you handle data preprocessing and ensure data quality?
  • Have you implemented any machine learning algorithms from scratch? If so, can you provide an example and explain your thought process?
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Meta MLE : Technical Screen


The technical screening will be conducted over the phone with an engineer. The candidate will be coding remotely on CoderPad, so it is important to practice using it to code beforehand.

The purpose of this round is to assess how quickly you can come up with the most efficient solution to a problem in your head and quickly code it without any logical flaws. You will need to think of corner and edge cases before and while coding, and check your code at the end. The coding problem will be worked out by hand on the digital whiteboard so the interviewer can see the thought process and collaborate with you.

The content of the screening round could cover a variety of topics, but generally, interviewers want to assess your ability to develop original software in a short period of time. Interview topics may cover anything on your resume, including data structures such as arrays and lists, binary trees, hash tables, stacks and queues, and graphs, as well as algorithms such as search (iterator, binary, hash), sort (merge, quick, bucket), graph traversals (BFS, DFS), complexity, Big O notation, and recursion.

Additional nice-to-have data structures and algorithms include trie, heap, set, red-black trees, randomized quicksort, dynamic programming, heap sort, radix sort, spanning tree, and minimum cut. The coding problems may be posed as an ambiguous, real-world problem, and the candidate will need to interpret the problem and apply their knowledge to find a solution. Interviewers are looking for insight into your thought process, creative solutions, ability to work out more than one way to solve a problem, and ability to talk through your rationale for choosing a certain way to approach solving the problem.

Interview Questions

  • Can you walk me through how you would approach solving a coding problem?
  • How would you ensure that your code is optimized and free of logic flaws?
  • Can you provide an example of a challenging problem you solved with a creative solution?
  • What are your strengths when it comes to data structures and algorithms?
  • How do you handle ambiguity in problem-solving?
  • Can you explain the concept of Big O notation and its significance in coding?

Check out video guide that delves into the interview process and provides valuable tips tailored to each round of the interview

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Meta MLE : Onsite Interview Rounds


The final round for the Machine Learning Engineer role at Meta consists of multiple rounds of onsite interviews. You can expect the following rounds::

  1. Coding Interview - This interview assesses your coding skills. You will be given a coding problem and assessed on how they solve the problem, the structure and style of their code, and their ability to identify and catch bugs in their code. The interviewer will evaluate your thought process throughout the interview. You can expect leetcode questions in this round.
  2. System Design Interview - This interview focuses on assessing your ability to solve a non-trivial engineering design problem. The interviewer will present a high-level problem, and you will use a virtual whiteboard as a visual aid to drive the conversation. The interviewer will assess your performance on four focus areas: problem navigation, solution design, technical excellence, and technical communication.
  3. Machine Learning Design Interview - This interview assesses your ability to design a large-scale ML system. You will be given an open-ended question to design a predictive system, focusing on the machine learning side of things. You will need to gather requirements, think about data collection, and break down the components involved in building a large-scale ML system. The interviewer will assess your performance on five focus areas: problem navigation, training data, feature engineering, modeling, and evaluation & deployment.
  4. Behavioral Interview - This interview aims to assess if you will thrive in Meta's fast-paced and highly unstructured environment. The interviewer will ask about your background, passions in tech, and the impact they want to make. The interviewer will assess your performance on five signals that correlate with success at Meta, namely resolving conflict, growing continuously, embracing ambiguity, driving results, and communicating effectively.

Interview Questions

  • What are ways to counter overfitting?
  • Build a recommendation based engine.
  • Design ML system for posts’ comments.
  • Design a data structure that supports insert, delete, search and get random in constant time.
  • Find the next greatest element in an array - searching only to the right.
  • How do you test your ML models for production scale?
  • Given a tree, write a function to return the sum of the max-sum path which goes through the root node.
  • Given a Directed Acyclic Graph, write a function to return the length of the longest path.
  • You have a House, Well & Tree arranged in a large grid with empty spaces in between to show where you can go. How will you go from house to nearest well without hitting a tree? Assume you can only go up.down/left/right and not diagonally and cannot hit a tree else you backtrack.
  • Implement a function to construct a binary tree.
  • Write a code for the dot product of two sparse vectors.
  • Given two sparse matrices, how would you compute the dot product?
  • Given an infinite chessboard, find the shortest distance for a knight to move from position A to position B.
  • Given a binary image, count the number of 4-directional connected components.
  • Serialize and deserialize a binary tree.
  • How would you build, train, and deploy a system to detect if multimedia and/or ads contents being posted violate terms or contain offensive materials?
  • How do you solve a disagreement with a team member?

Tips to stand out in the Meta MLE Interview

When you are preparing for a Meta MLE interview - we’d recommend keeping the following in mind:

  • Research about Meta's company culture, values, and goals to align them with your career aspirations. You can check out Meta Life to know more about the company culture.
  •  Do as many coding questions as you can. Go to Program Creek and review the Top 10 algorithms for a coding interview. Try to pick a few of the classic examples to solve by hand and from scratch on a blank sheet of paper. 
  • Familiarize yourself with key data structures and algorithms.
  • Define and develop a framework of the problem as you see it. Ask for help or clarification and spend two to five minutes asking the interviewer about corner cases on the problem. This will ensure that you’ve understood the problem correctly.
  • If your interviewer gives you hints to improve your code, please run with them.
  • Most coding interview questions are designed to have reasonably elegant solutions. If you have if-else blocks and special cases everywhere, you might be taking the wrong approach. Look for patterns and try to generalize.
  • Ensure that you spend time planning your approach but remember you can always go brute force and then optimize from there.
  • Use the S.T.A.R. method to mentally organize your thoughts. This will provoke a well- thought-out and chronological action of events. Easy to describe, easy to follow.
  • Brush up on basic ML theory and algorithm details. Be comfortable with concepts like overfitting and regularization.
  • Practice converting intuitive ideas to concrete features. For example, “number of likes” can be a good feature suggestion, but a better feature might also involve normalization, smoothing, and bucketing.
  • Having a good tool set of several different algorithms and understanding the tradeoffs is helpful. For example, be able to explain the advantages of logistics regression compared to SVM.
  • Design from the ground up. Think about how you’d design a system that Meta (or another large tech company) already has. It’s a good exercise to think through the complicated, high-scale systems that you already use every day.

Responsibilities of a Machine Learning Engineer at Meta

The responsibilities of a Machine Learning Engineer at Meta across roles can broadly be seen as-

  • Propose, design, and implement high-performance ML platform solutions that significantly advance the deployment of models that serve millions of users.
  • Drive the team's goals & technical direction to pursue opportunities that make your larger organization more efficient.  For example, you may identify new machine learning techniques that could be used to optimize existing products or services, or develop new algorithms that can help streamline internal processes and workflows.
  • Understand industry & company-wide trends to help assess & develop new technologies. For example, you may evaluate emerging machine learning frameworks or libraries to determine if they could be beneficial for your organization.
  • Adapt standard machine learning methods to best exploit modern parallel environments such as distributed clusters, multicore SMP, and GPU. 
  • Develop highly scalable classifiers and tools leveraging machine learning, regression, and rules-based models. For instance, you may create a classifier that can accurately categorize user data based on specific criteria, such as demographics or user behavior, and develop tools that can automate the data processing and analysis required for this task. 
  • Partner & collaborate with organization leaders to help improve the level of performance of the team & organization.

Skills and Qualifications needed for Machine Learning Engineers at Meta

Some of the skills and qualifications that may be required for a Machine Learning Engineer at Meta include:

  • Build experience in systems software or algorithms, as this is a key foundation for machine learning engineering.
  • Develop proficiency in at least one programming language, such as Java, C/C++, Perl, PHP, or Python, which are commonly used in the field of machine learning engineering.
  • Gain experience in one or more of the following areas: machine learning, recommendation systems, pattern recognition, NLP, data mining, or artificial intelligence. This will help you understand the fundamentals of machine learning and how it can be applied to real-world problems.
  • Familiarize yourself with distributed computing platforms such as Hadoop/HBase/Pig or MapReduce/Sawzall/Bigtable/Hive/Spark. These tools are commonly used in machine learning projects and can help you develop highly scalable solutions.
  • Be proactive and take the lead in cross-functional initiatives. This would help you drive meaningful results and effectively communicate findings with leadership and product teams. This will ultimately lead to quick and effective action on data-driven insights.

Salary Ranges

The average salary for a Machine Learning Engineer at Meta is around $190,000 per year, with a range of $140,000 to $260,000 per year depending on experience and other factors. It's important to note that salaries can vary based on a number of factors such as location, specific job responsibilities, and level of experience.


The interview process for a Machine Learning Engineer role at Meta typically includes 3 primary rounds - the HR round, a technical round, and the final onsite interview rounds. The recruiter will conduct a technical screening to evaluate your knowledge and experience in machine learning. The technical screening will be conducted over the phone with an engineer. The purpose of this round is to assess how quickly you can come up with the most efficient solution to a problem in your head and quickly code it without logic flaws. The final primary round for the Machine Learning Engineer role at Meta consists of multiple rounds of onsite interviews.

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