Meta Machine Learning Engineer Interview Guide

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.

• 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?

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.

• 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?

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.

• 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?