FAANG+ interview questions (SWE, MLE, AI engineers, etc) + answers

If you're interviewing at FAANG, AI labs, and other top tech companies, it's essential to practice answering typical questions beforehand.

To help you strategically prepare, we've gathered real interview questions asked at FAANG, OpenAI, Anthropic, and many more companies in recent years. We also provide links to solutions and mock interview videos and point you to specific guides if you need to dive deeper.

Simply click on your role below and start practicing.

1. FAANG interview questions for software engineers
2. FAANG interview questions for engineering managers
3. FAANG interview questions for AI engineers
4. FAANG interview questions for machine learning engineers
5. FAANG interview prep

1. FAANG interview questions for software engineers

Software engineer candidates at FAANG and AI labs have to face several rounds of interviews that include coding, system design, and behavioral questions.

Let's look at a sample of questions for each.

• Coding
• System design
• Behavioral

1.1 FAANG software engineer interview questions: coding↑

If you're applying to FAANG+, you'll face coding problems both in the phone screen and at the onsite/final stage, where you're likely to face two 45-minute coding interviews.

They will test your knowledge of common data structures and algorithms, largely focusing on Graphs, Trees, Arrays, Strings, and Dynamic programming.

All the questions below appeared in interview reports on Glassdoor.com from software engineer candidates at Meta, Amazon, Apple, Netflix, Google, and OpenAI. We just added links to good solutions that we found.

Google:

• Given a binary tree, find the maximum path sum. The path may start and end at any node in the tree. (Solution)
• Given a robot cleaner in a room modeled as a grid. Each cell in the grid can be empty or blocked. The robot cleaner with 4 given APIs can move forward, turn left or turn right. Each turn it made is 90 degrees. When it tries to move into a blocked cell, its bumper sensor detects the obstacle and it stays on the current cell. Design an algorithm to clean the entire room using only the 4 given APIs shown below." (Solution)
• Given an encoded string, return its decoded string." (Solution)
• Implement a SnapshotArray that supports pre-defined interfaces (note: see link for more details). (Solution)
• In a row of dominoes, A[i] and B[i] represent the top and bottom halves of the i-th domino.  (A domino is a tile with two numbers from 1 to 6 - one on each half of the tile.) We may rotate the i-th domino, so that A[i] and B[i] swap values. Return the minimum number of rotations so that all the values in A are the same, or all the values in B are the same. If it cannot be done, return -1. (Solution)
• Given a matrix and a target, return the number of non-empty submatrices that sum to target.(Solution)
• Given a rows x cols binary matrix filled with 0's and 1's, find the largest rectangle containing only 1's and return its area. (Solution)
• A strobogrammatic number is a number that looks the same when rotated 180 degrees (looked at upside down). Find all strobogrammatic numbers that are of length = n. (Solution)
• "A group of two or more people wants to meet and minimize the total travel distance. You are given a 2D grid of values 0 or 1, where each 1 marks the home of someone in the group. The distance is calculated using Manhattan Distance, where distance(p1, p2) = |p2.x - p1.x| + |p2.y - p1.y|." (Solution)

Meta:

• Given an array nums of n integers where n > 1,  return an array output such that output[i] is equal to the product of all the elements of nums except nums[i]. (Solution)
• Given a string S and a string T, find the minimum window in S which will contain all the characters in T in complexity O(n). (Solution)
• Given a non-empty string s, you may delete at most one character. Judge whether you can make it a palindrome. (Solution)
• Given the root node of a binary search tree, return the sum of values of all nodes with value between L and R (inclusive). (Solution)
• Given a Binary Tree, convert it to a Circular Doubly Linked List (In-Place). (Solution)
• Serialize and deserialize a binary tree (Solution)
• Given a binary tree, find the maximum path sum. (Solution)
• Given a list of non-negative numbers and a target integer k, write a function to check if the array has a continuous subarray of size at least 2 that sums up to the multiple of k, that is, sums up to n*k where n is also an integer. (Solution)
• You are given two non-empty linked lists representing two non-negative integers. The digits are stored in reverse order and each of their nodes contains a single digit. Add the two numbers and return it as a linked list. (Solution)
• We have a list of points on the plane.  Find the K closest points to the origin (0, 0). (Solution)
• A linked list is given such that each node contains an additional random pointer that could point to any node in the list or null. Return a deep copy of the list. (Solution)

Amazon:

• Given preorder and inorder traversal of a tree, construct the binary tree. (Solution)
• Design an algorithm to serialize and deserialize a binary tree. There is no restriction on how your serialization/deserialization algorithm should work. You just need to ensure that a binary tree can be serialized to a string and this string can be deserialized to the original tree structure." (Solution)
• Given a list of airline tickets represented by pairs of departure and arrival airports [from, to], reconstruct the itinerary in order. All of the tickets belong to a man who departs from JFK. Thus, the itinerary must begin with JFK." (Solution)
• Given a string s, find the longest palindromic substring in s. You may assume that the maximum length of s is 1000. (Solution)
• Say you have an array for which the i^th element is the price of a given stock on day i. If you were only permitted to complete at most one transaction (i.e., buy one and sell one share of the stock), design an algorithm to find the maximum profit. Note that you cannot sell a stock before you buy one. (Solution)
• Given an array of integers nums and an integer target, return indices of the two numbers such that they add up to target. You may assume that each input would have exactly one solution, and you may not use the same element twice." (Solution)
• Merge two sorted linked lists and return it as a new sorted list. The new list should be made by splicing together the nodes of the first two lists. (Solution)
• Design a stack that supports push, pop, top, and retrieving the minimum element in constant time. (Solution)

Netflix:

• Implement a rate limiter (Solution)
• Write a function to parse a log file and identify error patterns (Solution)
• Build a basic streaming pipeline that processes events in order
• Implement an LRU cache

Apple:

• Reverse a singly linked list (Solution)
• Suppose an array sorted in ascending order is rotated at some pivot unknown to you beforehand (e.g., [4,5,6,7,0,1,2]). Search for a target value and return its index, or -1 if not found. Required time complexity: O(log n). (Solution)
• Given n non-negative integers representing an elevation map where the width of each bar is 1, compute how much water it can trap after raining.
• Given an m x n binary matrix mat, return the distance of the nearest 0 for each cell. The distance between two cells sharing a common edge is 1.

OpenAI:

• Implement an LRU cache that supports get(key) and put(key, value), evicting the least recently used entry when capacity is exceeded. (Solution)
• Implement a key-value store with get, set, and delete operations where values are lists of tuples. Follow up: add sorting and merging across entries. (Solution)
• Implement a versioned key value store that supports put(key, value), get(key), and get(key, timestamp), returning the value for a key as it existed at a given point in time.
• Given a list of words sorted according to the rules of an alien language, determine the order of characters in that language.
• Given an n×n grid of 1s and 0s, return the number of islands.
• Given the root of a tree, count the number of nodes that satisfy a given condition.
• Topologically sort a directed acyclic graph and detect cycles.

NVIDIA:

• Write a BFS for a generic graph.
• Write a BFS on a "sorted" binary tree, which prints nodes values in level order.
• Write as many functions as possible that take an 'X' or 'Y' as input and return the opposite.
• Find in the array two elements whose sum is equal to a given constant.
• Create a linked list struct with some of the operations in C language.
• Design and implement an LRU cache with a maximum size and basic get+set operations.
• Sort an array (given) in o(n) runtime.
• Find in the array 2 elements whose sum is equal to a given constant.

1.2 FAANG software engineer interview questions: system design↑

System design interviews at FAANG and Big Tech companies are notorious. The questions are challenging, open-ended, and unlike any other interview round.

Below are the most common system design questions reported on Glassdoor from FAANG, OpenAI, and Anthropic candidates. We've linked to the best free example solutions we've been able to find online.

Google:

• Design a web cache
• Design Google Maps
• Design a task-scheduling feature
• Design YouTube search
• Design Google Search
• How would you design a system for a robot to learn the layout of a room and traverse it?
• Design a distributed ID generation system
• Design the server infrastructure for GMail

Meta:

• How would you design Instagram / Instagram Stories?
• Design WhatsApp / Facebook Messenger
• Design Facebook status search or Facebook newsfeed
• Design an online collaborative editing tool
• How would you design an autocomplete service for a search engine?
• Design a file system

Amazon:

• Design a parking lot
• Design a phone billing system
• Design a TinyURL service
• Design an API that would take and organize order events from a web store
• Design a product recommendation system based on a user's purchase history
• How would you design an electronic voting system?
• Design a deck of cards
• Design a system to optimally fill a truck
• Design a warehouse system for Amazon

OpenAI:

• Design the infrastructure to serve ChatGPT at hundreds of millions of weekly users.
• Design an LLM-powered enterprise search system that supports natural language queries across internal documents with role-based access control.
• Design a GPU scheduling system to allocate compute resources across competing workloads at scale.
• Design a vector database to store and search billions of embeddings efficiently.
• Design a system to detect NSFW content in real-time ChatGPT outputs. Address model selection, latency requirements, and the feedback loop.

NVIDIA:

• How would you design a chatbot service that provides users with a variety of information?
• How would you perform API modeling while managing multiple servers?
• Create a memory management system that allocates fixed-size blocks, in a constrained environment with limited memory. Avoid using malloc, free, new, or delete; instead, rely on a 'memory' function for both managing memory and allocating space for clients.
• How would you design a memory allocator?

Anthropic:

• Design the Claude chat service.
• Design a distributed search system for 1 billion documents at 1 million QPS. Cover sharding, caching, and LLM inference scaling.
• Design APIs for developers to access Anthropic's AI models securely and efficiently.
• Design a file-sharing / distribution system.
• Design a high-concurrency inference API / parallel processing pipeline.

 

1.3 FAANG software engineer interview questions: behavioral↑

To get an offer at a top tech company, strong technical skills are not enough. You also need to show excellent collaboration skills and demonstrate some leadership capacity.

FAANG, emerging tech, and AI labs test you for these soft skills using behavioral questions. Let's take a look at some of the most frequently reported on Glassdoor.

Google:

• Tell me about yourself
• Why Google?
• Tell me about a time you failed
• Tell me about a time you had to resolve a conflict in a team
• What is your favorite Google product?

Meta:

• Tell me about yourself.
• Why Facebook/Meta?
• Tell me about a recent/favorite project and some of the difficulties you had
• Tell me about the greatest accomplishment of your career.
• Tell me about a time you had to resolve a conflict in a team

Amazon:

• Tell me about a time you had to change your approach because you were going to miss a deadline
• Tell me about a time you did something at work that wasn't your responsibility / in your job description
• Tell me about a time you had a conflict with a coworker or manager and how you approached it
• What is the most innovative idea you've ever had?
• Tell me about a project in which you had to deep dive into analysis
• Why Amazon?

Netflix:

• Tell me about a time you had to make a decision with incomplete information
• Describe a technically complex project you worked on. What trade-offs did you make?
• Tell me about a time you had to push back on a product requirement
• How do you decide when code is "good enough" to ship?

Apple:

• How would you handle a technical conflict between team members?
• Tell me about a time when you had too many things to do, and you were required to prioritize your tasks.

OpenAI:

• Tell me about a time you solved a particularly complex technical problem. What was your approach?
• Describe a situation where you had to make a difficult technical trade-off decision.
• Tell me about a time you influenced a technical decision without having direct authority over it.
• Tell me about a time you made a mistake. What happened and what did you take away from it?
• How do you manage multiple conflicting priorities?
• Tell me about a time you and a cross-functional partner disagreed. How did you resolve it?

Anthropic:

• Talk about a past project you've worked on.
• Walk me through a project you owned end-to-end. What were the key technical decisions?
• Tell me about a technical misjudgment that delayed a project.

NVIDIA:

• Why do you want to work at NVIDIA? Why did you apply to this role?
• Tell me about the most challenging project you've worked on.
• Tell me about a time you had to make a big decision for a crucial client project.
• Can you describe a challenging technical problem you faced during a software development project, how you approached it, and what steps you took to overcome it?

2. FAANG interview questions for engineering managers↑

Engineering manager candidates at FAANG and other top tech companies generally face a mix of people management, project management, fit, system design, and coding questions.

Depending on your level and company, you can expect two or even three system design interviews. You'll probably face fewer coding questions than a regular software engineer would. For instance, Amazon tends not to bother asking SDM candidates any coding problems). Google, on the other hand, has a code review round for EMs.

Below we list questions reported by engineering manager candidates at Google, Meta, Amazon, Netflix, and Apple across the categories of people management, project management, and behavioral/"fit".

If you're looking for system design or coding questions, use the lists above, which are equally valid for engineering managers as for software engineers (though the expectations regarding how you tackle the questions will, of course, be different).

• People management
• Project management
• Behavioral / fit

2.1 FAANG engineering manager interview questions: people management↑

Google:

• How do you deal with low/high performers?
• How do you handle conflicts?
• How do you handle people who are not team players?
• Tell me about a time you developed and retained team members
• How do you set a vision for your team?

Meta:

• How do you manage your team’s career growth?
• How do you manage difficult conversations?
• How do you manage underperforming employees?
• What would you do with someone who had stayed at the same level for too long?

Amazon:

• Give an example of how you helped another employee
• How do you manage low performers?

Apple:

• How do you manage people? Walk me through your management philosophy.
• Why do you want to pursue a career in people management? What was your most rewarding experience in people management?

Netflix:

• What experience do you have building and maintaining a diverse candidate pool for open roles?
• How do you resolve conflict?

2.2 FAANG engineering manager interview questions: project management↑

Google:

• As a manager, how do you handle trade-offs?
• Describe how you deal with change management
• Describe in detail a project that failed
• Describe a project in the past that was behind schedule and provide concrete steps that you took to remedy the situation
• Tell me how you would balance engineering limitations with customer requirements

Meta:

• Describe the most technically complex project that you have worked on and why it was complex
• Describe a software development project you led and your approach
• Tell me about a project, product, or system you worked on. What were the design and technical problems you faced? How did you solve them?

Amazon:

• What was the largest project you've executed
• Tell me about a time you needed to deliver a project on a deadline, but there were multiple roadblocks and constraints to deliver. How did you manage that situation?

Apple:

• If you have 2 clients requesting very different features for the same product, how would you prioritize them?
• What's one time you didn't have the technical knowledge for a solution and had to bridge the gap? 

2.3 FAANG engineering manager interview questions: behavioral / fit↑

Google:

• Why are you an effective R&D leader?
• Tell me about yourself
• Why Google?

Meta:

• Why Meta?
• Tell me about a mistake you made and the lesson you learned from it. 
• Tell me about what you've been working on over the last year
• Tell me about yourself
• Why are you leaving your current job?
• How do you communicate about technical project needs with non-technical teams?

Amazon:

• When was the last time you did something innovative?
• Give an example where you failed to do the right thing
• Tell me about a time you had a conflict with your supervisor and how you resolved it.

Apple:

• Tell me about a time when you had to make a difficult decision with incomplete information. How did you handle it, and what was the outcome?
• What does success mean to you?

Netflix:

• How did you come up with the most innovative idea you've ever implemented? How did you implement it?
• Tell me about the area where you have the most to learn

3. FAANG interview questions for AI engineers↑

The AI engineer role is relatively new. Most companies are still figuring out how to best screen for the role, so you’ll need to cover a lot of ground in your prep, especially if you’re interviewing at multiple companies.

Expect coding and system design rounds similar to software engineer interviews, in addition to rounds testing your proficiency and experience in AI, machine learning, deep learning, applied infrastructure, and LLM engineering.

Below are the 6 most common question types we’ve found, based on reports from Glassdoor across different companies, including Google, LinkedIn, Deloitte, and more.

Note: this section was written with insights from interview coach Viral (Meta Engineering Leader).

• ML / DL fundamentals
• Applied ML / ML infrastructure
• LLM engineering & RAG
• Coding
• AI system design
• Behavioral

3.1 FAANG interview questions for AI engineers: Machine learning and deep learning fundamentals↑

To ace your AI engineer interviews, you need to demonstrate foundational knowledge in machine learning, deep learning, and other related concepts. Expect questions testing your knowledge of the different machine learning models and neural networks, generative AI, bias-variance trade-offs, and handling overfitting.

Example FAANG AI engineer interview questions: ML/DL fundamentals

• Precision vs Recall — which matters more for fraud detection?
• Explain precision/recall tradeoff.
• What is F1 score?
• What are underfitting and overfitting, and the tradeoff of each?
• Explain the significance of the ROC (Receiver Operating Characteristic) curve in machine learning.
• How to deal with overfitting?
• Give your detailed view about linear regression.
• Choose Linear Regression or Logistic Regression. Explain your pick and how you implement it.
• Explain Gradient Descent in machine learning.
• What algorithms would you use for a classification problem?
• What is your favourite ML model?
• Can you explain the basic principles behind Generative Adversarial Networks (GANs)?
• Explain the architecture and components of a Convolutional Neural Network (CNN)
• Tell me about BERT (Bidirectional Encoder Representations from Transformers) architecture.

3.2 FAANG interview questions for AI engineers: Applied machine learning & machine learning infrastructure ↑

As an AI engineer, you’ll be working with foundational models, finding ways to apply them to existing systems and APIs for problem-solving and optimization. You’ll also need to handle the ML infrastructure necessary for the deployment and scaling of these models.  Be prepared to field questions on your hands-on ML application and infrastructure experience. 

Example FAANG AI engineer interview questions: Applied ML & ML infrastructure 

• Explain a project where you implemented a machine learning model.
• How do you deal with certain situations of model training?
• Explain RNN (Recurrent Neural Network) Transfer Learning.
• What is finetuning? How would you fine-tune X company’s model which uses hosted LLM models?
• Describe any AI pipelines that you implemented in any cloud.
• What are the trade-offs between using FP32, FP16, and BF16 precision when training large-scale models on NVIDIA GPUs?
• When dealing with massive datasets, how do you decide between using sparse matrix representations versus dense ones, and how does this affect memory bandwidth?

3.3 FAANG interview questions for AI engineers: LLM engineering and RAG↑

Most, if not all, AI engineer roles will involve large language model (LLM) engineering, with a heavy focus on Retrieval-Augmented Generation (RAG). So demonstrating experience in both will give you a leg-up. 

Example FAANG AI engineer interview questions: LLM engineering and RAG

• What is a RAG pipeline and how to design it using Python code?
• How do you deal with hallucination in LLMs?
• How are OpenAI embeddings different from normal deep learning embeddings?
• What is semantic searching?
• What are your metrics for evaluating RAG performance?
• How would you optimize a RAG system that is currently returning noise, which increases context entropy and causes attention decay in the token sequence?

3.4 FAANG interview questions for AI engineers: Coding (Python, SQL, DSA)↑

If you look at many open AI engineer roles, you’ll find that most AI engineer roles will be software engineer roles with a focus on AI/ML. These roles typically require Python proficiency, DSA knowledge, and SQL experience for handling datasets. So it’s best to prepare for rounds testing for these skills.

Example FAANG AI engineer interview questions: Coding (Python, SQL, DSA)

• Python: immutable vs mutable variables passed to a function — how different are they?
• Python: what is the difference between is and ==?
• How would you build reproducible code?
• Write a code in Python to return a sorted order of a list of odd numbers extracted from a list of numbers. Do not use inbuilt sorted function. (Solution)

3.5 FAANG interview questions for AI engineers: AI system design↑

Whether hiring for an AI or ML engineer, companies are looking for AI system builders. In particular, they want to know if you can build reliable systems and have a deep understanding of how a system might fail (and what you plan to do about it if it happens).

Example FAANG AI engineer interview questions: AI system design

• How would you integrate an AI-based system to help a zoo owner improve their business?
• Design a recommender system.
• Design and implement a full system solution.

3.6 FAANG interview questions for AI engineers: Behavioral questions↑

Based on our analysis of reported questions, AI engineers get a lot of AI-related behavioral questions, on top of the standard ones. You may also get hypothetical questions surrounding the ethics of AI, so be sure to read up on the topic beforehand.

Example FAANG AI engineer interview questions: Behavioral

• What is one unexpected challenge you ran into while working, and how did you tackle it?
• How do you stay up to date with technical trends?
• Tell me about a time you used data or experimentation to drive a decision in a high-ambiguity environment.
• How would you explain a complex AI system to a non-technical stakeholder and get buy-in?

4. FAANG interview questions for ML engineer↑

Machine learning (ML) engineer candidates at FAANG and other top tech companies have to face several rounds of interviews similar to the SWE role, with the addition of ML fundamentals.

Expect two coding challenges as early as your tech screen, and then prepare for more in your interview loop. For ML system design, you’ll likely face more than one round, depending on your target level. Behavioral interview rounds will depend on the company and level as well.

One additional round for the ML engineer is the ML fundamentals round. Most companies will usually have one. If you’ve worked on an ML project before, expect your interviewer to ask you for a deep dive.

If you’re looking for coding questions, use the lists under SWE and AI engineers, as those are likely relevant for this role as well. However, the expectations regarding how you tackle the questions will, of course, be different.

For behavioral questions, we recommend looking at the lists of questions under SWE and AI engineers as you'll likely receive similar questions.

• ML system design
• ML fundamentals / domain

4.1 FAANG interview questions for ML engineers: ML system design

The ML system design interview is a core part of the ML engineer interview process for mid-level to senior engineers. You’ll need to show that you can build useful, working ML systems that solve real-world problems. 

Google:

General

• How would you build, train, and deploy a system that detects if multimedia and/or ad content violates terms or contains offensive materials? 
• Design autocomplete and/or spell check on a mobile device.
• Design autocomplete and/or automatic responses for email.
• Design the YouTube recommendation system.

Follow-up questions

• How would you optimize prediction throughput for an RNN-based model?
• What loss function will you optimize and why?
• What data will you collect to train your model and why?
• How will you avoid bias and feedback loops?
• How will you handle a corrupt model or an incorrect training batch?

Meta:

• Design a personalized news ranking system. 
• Design a product recommendation system. 
• Design an evaluation framework for ad ranking.

Amazon:

• Design a system that recommends in-flight movies from a database, such that the total time matches directly with the flight time.
• Design a system that recommends clothing to consumers.
• Design a product recommendation system.
• Design autocomplete and/or spell check on a mobile device.

OpenAI:

• Design the infrastructure to serve ChatGPT at hundreds of millions of weekly users.
• Design an LLM-powered enterprise search system that supports natural language queries across internal documents with role-based access control.
• Design a GPU scheduling system to allocate compute resources across competing workloads at scale.
• Design a vector database to store and search billions of embeddings efficiently.
• Design a system to detect NSFW content in real-time ChatGPT outputs. Address model selection, latency requirements, and the feedback loop.

Apple: 

• Design a recommendation system for Apple Music
• How would you build a machine learning model to predict customer churn for Apple's subscription services (Apple TV+, iCloud+)?
• How would you build a photo classification model that runs on-device with real latency and memory constraints?
• How would you evaluate the performance of a recommendation algorithm for Apple Music?
• What features would you prioritize when building a content recommendation model for Apple Fitness+?

Anthropic:

• Design the Claude chat service
• Design a distributed search system for 1 billion documents at 1 million QPS. Cover sharding, caching, and LLM inference scaling.
• Design a batched inference system where 100 requests take the same time as 1. Use a queue to batch requests.
• Design a system that enables a large language model to handle multiple questions in a single thread
• Design APIs for developers to access Anthropic's AI models securely and efficiently
• Design a file-sharing / distribution system
• Design a high-concurrency inference API / parallel processing pipeline

4.2 FAANG interview questions for ML engineers: ML domain/fundamentals↑

The ML engineer interview will typically have a round for ML domain questions. The approach differs per company. Amazon treats this round like an ML quiz to test the breadth of your knowledge. Apple goes deeper into theory. Depending on the team, you may cover classical ML concepts, deep learning fundamentals, and applied ML problem-solving. At Google, you may be asked about the basics or about an ML/DL project you’ve worked on, depending on your target level and what’s on your resume.

Google:

• What are the most important algorithms, programming terms, and theories to understand as a machine learning engineer?
• Can you describe a machine learning project you have worked on and the impact it had?
• How would you explain machine learning to someone who doesn't understand it?
• How to write a neural network in PyTorch
• How to deploy a model in cloud providers like GCP and AWS
• When do you deal with overfitting (dropout, weight decay, augmentation)?
• When do you stop training a model?
• How do you stay up to date with the latest news and trends in machine learning?

Apple:

• Explain the bias-variance tradeoff and how you'd think about it when selecting a model at scale
• What is the difference between fine-tuning and retrieval-based methods like RAG? When would you use each? 
• Walk through the transformer architecture and explain the self-attention mechanism 
• How do you handle class imbalance in a training dataset?
• Implement the forward and backward pass of a custom function in PyTorch to enable backpropagation
• What are the tradeoffs between on-device inference and server-side inference for a feature like Siri?
• Walk through how convolution works and where you'd apply it in a model architecture 
• When do you stop training a model?
• How do you deal with a large dataset where only a small fraction of examples are labeled?

Amazon:

• What is a K-means algorithm?
• What is the difference between SVM and logistic regression?
• Describe normalization and Bayes’ rule.
• Describe linear regression versus logistic regression.
• What kind of different loss functions do you know?
• How do you measure the performance of computer vision models?
• How do you deal with a troublesome dataset?
• How do you deal with misrepresentative training data (imbalanced dataset, overfitting, explain how L1/L2 regularization work at an optimization level)?
• How do you deal with a large dataset where only a few examples are labeled (semi-supervised learning)

5. Preparing for FAANG interviews↑

Now that you know what questions to expect, let's focus on preparation.

Below, you'll find links to free resources and four introductory steps to help you prepare for your FAANG interviews.

If you want a full breakdown of every stage first, our guide on the FAANG interview process and timeline walks you through all seven stages, from resume screen to offer, with company-specific notes for Meta, Google, Amazon, Apple, and more. You can also check our guide on how to know if you're ready for a FAANG interview for a two-step self-assessment.

5.1 Deep dive into the company and its products

As you've probably figured out from the example questions listed above, you can't make it into a FAANG company without being familiar with its products and the organization. You'll therefore need to do some homework before your interviews.

Here are some resources to help you get started with this:

Meta:

• Meta interview process (by IGotAnOffer)
• Meta's 6 core values (by Meta)
• Facebook’s “hacker culture” (by Mark Zuckerberg, via Wired)
• Meta annual reports and strategy presentations (by Meta)
• Meta's approach to tech trends (by CB Insights)
• Meta org culture analysis (by Panmore Institute)

Amazon:

• Amazon interview process (by IGotAnOffer)
• Amazon's technology culture video mix (by Amazon)
• Amazon vision and mission analysis (by Panmore Institute)
• Amazon strategy teardown (by CB Insights)

Google:

• Google interview process (by IGotAnOffer)
• Alphabet annual reports and strategy presentations (by Alphabet)
• Google strategy teardown (by CB Insights)
• Google org culture analysis (by Panmore Institute)

Apple:

• Apple interview process (by IGotAnOffer)
• Apple Strategy Teardown (from CB Insights)
• Apple’s Company Culture: An Organizational Analysis (by Panmore Institute)
• Apple’s Marketing Mix: 4P Analysis (by Panmore Institute)

Netflix:

• Netflix interview process (by IGotAnOffer)
• Netflix Culture
• Netflix Product Strategy: A 2020 Case Study by Gibson Biddle (Previous VP of Product at Netflix)
• Netflix Techblog
• Netflix Annual Reports and Proxies

OpenAI:

• OpenAI interview process (by IGotAnOffer)
• OpenAI Charter
• OpenAI research and product blog
• OpenAI's interview guide
• OpenAI careers and culture page

Anthropic:

• Anthropic interview process (by IGotAnOffer)
• Anthropic's core views on AI safety 
• Constitutional AI: harmlessness from AI feedback 

NVIDIA:

• NVIDIA interview process (IGotAnOffer)
• Nvidia annual reports and proxies
• Nvidia employee content and testimonials
• Nvidia’s innovation strategy

5.2 Learn a consistent method for answering FAANG interview questions

It's a lot easier to answer FAANG interview questions when you're ready with a framework to structure your answer on. You can use the question guides we've linked to throughout this article to learn the frameworks and methods relevant to your role.

If you find that you need more practice, check out the question lists below. They cover frequently asked questions by role at each company, along with sample answers you can use to benchmark your own:

• Netflix interview questions
• Meta interview questions
• Google interview questions
• OpenAI interview questions
• Anthropic interview questions

Once you’re in command of the subject matter, you’ll want to practice answering questions. But by yourself, you can’t simulate thinking on your feet or the pressure of performing in front of a stranger. Plus, there are no unexpected follow-up questions and no feedback.

That’s why many candidates try to practice with friends or peers.

5.3 Practice with peers

If you have friends or peers who can do mock interviews with you, that's an option worth trying. It’s free, but be warned, you may come up against the following problems:

• It’s hard to know if the feedback you get is accurate
• They’re unlikely to have insider knowledge of interviews at your target company
• On peer platforms, people often waste your time by not showing up

For those reasons, many candidates skip peer mock interviews and go straight to mock interviews with an expert. 

5.4 Practice with experienced tech interviewers

In our experience, practicing real interviews with experts who can give you company-specific feedback makes a huge difference.

Find a FAANG interview coach so you can:

• Test yourself under real interview conditions
• Get accurate feedback from a real expert
• Build your confidence
• Get company-specific insights
• Learn how to tell the right stories, better.
• Save time by focusing your preparation

Landing a job at a big tech company often results in a $50,000 per year or more increase in total compensation. In our experience, three or four coaching sessions worth ~$500 make a significant difference in your ability to land the job. That’s an ROI of 100x!

Click here to book FAANG mock interviews with experienced FAANG interviewers