39 AI Interview Questions (and Answers) To Help Your Prep

If you’ve been dreaming of a career in artificial intelligence (AI), the job interview is the last hurdle you’ll face on your journey. It can also be the most intimidating part of the process because of the pressure that you might feel about facing recruiters and dealing with the sometimes tricky questions that can come your way. 

The good news is that there are certain questions that tend to be repeated across AI interviews. We’ve put together a list of the most common ones in this article. If you prepare for these questions, you can rest assured that you can confidently answer most of what comes your way in an AI interview. 

What Can You Expect in an AI Interview?

AI interviews are a mix of technical and personality-based questions. Since AI is an emerging field, you can expect questions that pertain to recent developments in the field and the reason for your interest in it. 

The technical part of the interview process will usually cover basic concepts in artificial intelligence. You should also be prepared to complete a coding challenge, where you will be required to implement programs with AI capabilities. In certain cases, you might also have to complete a case-based round where you’re given between three and five days to solve a problem.

Recruiters will also want to see that you have soft skills. You should be able to talk about things like your strengths and weaknesses and the lessons you’ve learned from previous jobs. These answers are used to determine whether you’re a cultural fit at a given company. 

What specific questions can you expect in an AI interview? Let’s find out. 

Artificial Intelligence Interview Questions: Technical and Industry-Related

Here’s a look at some of the core artificial intelligence questions that you should prepare for if you’re heading into the interview process. 

How Would You Describe AI to a Ten-Year-Old?

The purpose of this question is to gauge whether you have a fundamental understanding of artificial intelligence. Although the question mentions a ten-year-old, you don’t need to dumb your answer down. Rather, the goal is to explain the essence of AI in simple language.

Here’s how you might phrase your answer: 

Artificial intelligence is a kind of software that can complete tasks using its own intelligence. Usually, we tell computers exactly how to perform a task. We do that using a programming language, which is a language that a computer can understand. But in the case of artificial intelligence, we give the computer a task and let it figure out on its own how that task should be completed. 

What Are Intelligent Agents?

An intelligent agent is an entity that can make autonomous, rational decisions by interacting with its environment. These interactions are facilitated by components such as sensors, effectors, and actuators. 

Human beings are considered intelligent agents. In the same way, certain kinds of robots are also intelligent agents. Think about how autonomous cars can sense their environment and determine whether to keep accelerating, what speed to maintain, and so on. 

There can also be software-based intelligent agents. Voice assistants like Siri and Alexa are prime examples. They gather data from verbal information and then make decisions about what commands need to be executed. 

What Are AI Neural Networks?

Neural networks are a method in artificial intelligence that is structured using inspiration from human intelligence. In the same way that human brains have neurons that are interconnected, artificial neural networks have nodes connected from an input to an output layer, which creates an intelligent, adaptive software system. 

There are three categories of layers that form a neural network: input layer, hidden layers, and output layer. The input layer takes information in from an external source. The incoming data is categorized and processed at this stage. 

The data obtained from the input layer is put through a set of hidden layers. The hidden layers are where multiple levels of processing happen. Finally, we have the output layer, which produces the result of the neural networking process. This output layer may often be used to come up with the input features for another stage of the iterative training process. 

What Is the Difference Between Strong AI and Weak AI?

Think of strong AI as the ultimate goal of the field of artificial intelligence. This implies a hypothetical computing system that exactly mimics human intelligence and information processing. Essentially, it would be a computer that is indistinguishable from a human being at a cognitive level. 

Weak AI is a lot more tame in comparison. Weak AI systems are those that use artificial intelligence techniques to solve highly complex problems. Any true AI system that we use today, like a self-driving car or a voice assistant, is an example of weak AI. 

Describe Machine Learning and Deep Learning. What Is the Difference Between Machine Learning, Deep Learning, and AI?

Machine learning is a field within artificial intelligence that deals with computing systems that can learn from given datasets. The basic idea behind machine learning is that you provide a program with a dataset and allow it to learn from it. That means that it finds patterns in the data and makes learned conclusions without human intervention. 

Deep learning is a technique within machine learning that makes use of an artificial neural network to mimic human brain processes. The focus of deep learning models is to come up with information-processing constructs that can gather and process data in the same way that humans do. Deep learning can be considered a subset of machine learning. 

Artificial intelligence is a field that encompasses machine learning and deep learning. The goal of artificial intelligence is to create software that can think and solve problems like human beings do. 

What Is the Turing Test?

The Turing test is a test for machine intelligence that was proposed by the computer scientist Alan Turing. 

The test is structured with three main entities: an interrogator, a machine, and a human being. The interrogator knows the human being and the machine only through labels, like Player A and Player B. That means that they can interact with the human and the machine, but can’t tell them apart at the beginning of the game. 

The interrogator communicates with the human being and the machine through text messages via a keyboard. They are allowed to interact with both interlocutors through these messages. The goal of the game is for the interrogator to correctly identify the human being and the machine. 

What Is Game Theory? Why Is It Important to AI?

Game theory is a field of mathematics that is relevant to various disciplines, including economics and artificial intelligence. The field essentially studies how agents, which are rational actors, would behave in a game that has a certain set of predefined rules. Given that set of rules, game theory analyzes different strategies and rewards that come into play. 

Game theory can also be applied to study various interactions that occur in artificially intelligent systems. Consider the example of a generative adversarial network, which consists of a generator component and a discriminator component. The interactions between these two components can be studied and manipulated based on insights from game theory. Principles from the field of game theory can be similarly applied to other AI methods. 

What Is Cloud Computing?

Cloud computing is a new paradigm in computing that lets you use the resources of a remote data center over the Internet. These computing resources include things like storage, servers, networking tools, and applications. 

What Is NLP? What Are the Components of NLP?

NLP stands for Natural Language Processing. It is a branch of artificial intelligence that’s concerned with studying how computers can understand, process, and manipulate human language. Voice assistants like Siri use natural language processing algorithms for speech recognition purposes. 

There are two components to NLP technology. The first is Natural Language Understanding (NLU), which is the processing by which computers translate human language into simpler parts like keywords, semantic constructs, and relations. This is where the speech recognition part happens. 

The other component of NLP is Natural Language Generation (NLG), which can be thought of as the opposite of natural language understanding. NLG allows computers to translate machine data into natural language through processes like sentence planning and text planning. 

What Are Some Ethical Concerns Relating to AI?

The following are the main concerns pertaining to artificial intelligence development. 

• Lack of transparency: Most AI is currently a black box, and nobody besides the developer knows how exactly an artificially intelligent software does what it does. This lack of transparency is problematic because it hides the true potential and dangers of AI systems from public oversight. 
• Neutrality: Because AI systems are developed using existing data, the biases in those datasets can creep into the software. This can lead to these systems producing results that are not neutral. 
• Surveillance: AI systems are incredibly powerful in their ability to assess their environment and gather data from it. This raises concerns about the ability of such machines to be used for surveillance purposes. 
• Employment: AI makes it possible to automate a lot of the jobs that are currently done by human agents. As more and more automation becomes possible, society will have to deal with the question of unemployment and the changing nature of work. 

Can You Name the Properties of a Good Knowledge Representation System?

The following are the properties of a good knowledge representation system: 

• Representational Accuracy: Representational systems deal with various types of knowledge, such as procedural knowledge, heuristic knowledge, and declarative knowledge. A good system should be able to represent all these different kinds of knowledge accurately. 
• Inferential Adequacy: The system should be able to make inferences from the existing knowledge in the system to produce new knowledge. 
• Inference Efficiency: This refers to the system’s ability to take the new knowledge it has produced and channel it into guides in a productive manner. 
• Acquisitional efficiency: These systems use various techniques to acquire new knowledge. These methods should be optimized so that the knowledge acquisition rate can be frequently enhanced. 

What Is Collaborative Filtering? How Does It Differ From Content-Based Filtering?

Collaborative filtering is a method that’s used widely in the development of recommender systems. The basic approach in collaborative filtering is to group users based on their past interaction with a set of items—let’s say movies, for example. It then recommends new movies to users based on what other users in the same group liked or watched. 

Content-based filtering places a lot more emphasis on the nature of each user and the nature of the items being recommended. Since we’re talking about movies, a content-based artificial intelligence system would consider movie genre, length, director, and other such factors. It would also take into consideration the age, location, and other characteristics of users. The system then makes recommendations by matching the features of these user-item pairs. 

What Is Selection Bias? What Types of Other Bias Can You Encounter During Sampling?

Selection bias refers to any errors that arise when selecting the sample population for any kind of study or survey. Here are some other biases that can creep in when you’re doing sampling: 

• Survivorship bias: Placing an inordinate amount of focus on the people or groups who have passed certain selection criteria 
• Undercoverage bias: Underrepresenting certain kinds of groups in a given sample 
• Non-response bias: When the people who don’t respond in a survey or study constitute a group that is very different from the people who do 
• Observer bias: When the observer, or the person conducting the study, overestimates or underestimates certain results or trends

What Is a Random Forest? Could You Explain Its Role in AI?

“Random forest” refers to an algorithm that is commonly used in machine learning algorithms. It falls under the category of supervised machine learning algorithms. 

The random forest algorithm takes data from multiple decision trees as input. These decision trees are sourced from multiple subsets of a given dataset. The random forest takes the average of the values from these various decision trees as a way to enhance the learning rate for a given dataset. 

What’s an Eigenvalue? What About an Eigenvector?

An eigenvalue is a scalar value that corresponds to a group of linear equations. These linear equations are usually defined by a set of matrix equations. 

Eigenvectors are vectors that are associated with linear transformations. More specifically, they are used to describe those vectors that change at most by only a constant factor on the application of that linear transformation. 

What Are GANs? What Are the Two Components of GANs?

GAN stands for generative adversarial networks. A GAN is an artificial neural network architecture that’s used to analyze and classify the data in a dataset with a great deal of accuracy. Once this analysis is done, it becomes possible to generate more items similar to the ones in the given dataset. 

GANs are able to achieve that using an architecture that consists of two components: a generator and a discriminator. The generator is the part of the neural network that generates items that could plausibly be part of that dataset. Let’s say you’re trying to train a machine learning algorithm to identify fake notes. In that case, the generator would try to produce data that looks as much like a real note as possible. 

The discriminator is the part of the network that tries to tell the real and fake data apart. In the example we’ve taken, the generator would try to identify which the actual notes are and which ones are fake. 

In the initial stage of the training process, the generator produces poor results and the discriminator easily identifies the fakes. Every time this happens, the discriminator penalizes the generator. Over time, the generator learns from this process and begins to produce items that begin to look more and more like the real data. 

What Is a Hash Table?

A hash table is a kind of data structure. The data within it is stored in arrays with unique index values. Since these items in the array are indexed, hash tables make it very easy to search and manipulate items in this data structure. 

What Are the Different Algorithm Techniques You Can Use in AI and Machine Learning?

There are a whole host of algorithms that you can use in AI and machine learning. Let’s take a look at a few examples based on their categories. 

Supervised Learning

• Support Vector Machines
• Decision Trees
• Naive Bayes 

Semi-Supervised Learning

• Consistency Regularization
• Pseudo-labeling

Reinforcement Learning 

• Value iteration
• Markov decision process

Unsupervised Learning

• Principal Component Analysis 
• T-distributed Stochastic Neighbor Embedding 
• Hierarchical Clustering 

How Would You Go About Choosing an Algorithm To Solve a Business Problem?

There is no fixed way that you can go about choosing an algorithm to solve a business problem. That said, there are certain things that you can take into consideration to ensure that the algorithm that you choose can solve a problem with a high degree of efficiency. Here are a few strategies to do that:

Study the Problem

A mistake that artificial intelligence engineers often make is that they gloss over the details of the problem before they begin to ideate solutions. This is a disservice to the goal and the wrong approach to take. You should take your time to extensively study the nature of the business problem, determine whether it’s a strategic or operational issue, and document what you learn as part of your research. 

Consider Different Solutions

There will always be different algorithms that you can use to solve a single problem. In the initial stage, don’t go straight to choosing a single solution. Rather, take different kinds of algorithms into consideration based on whether you’re dealing with a problem that pertains to regression, classification, optimization, segmentation, or forecasting. 

Reflect on Your Choices

Always take some time during this process to challenge some of the assumptions that you’ve made along the way. Go back to the problem statement and look at what you’ve found in your initial research. Then, narrow down your choices based on what you’ve been able to gather. 

What Is Regularization?

Regularization is the process by which machine learning algorithms—both supervised and unsupervised learning methods—are optimized in order to avoid underfitting and overfitting functions. 

Underfitting and overfitting are two common ways in which errors creep into machine learning functions. The former occurs when machine learning algorithms haven’t been allowed to study a dataset for enough time. As a result, they don’t discover enough relationships between variables and cannot conduct the classification or prediction task accurately. 

Overfitting, on the other hand, is a result of having a machine learning algorithm look at a dataset too many times. This leads to a situation where it will find unnecessary patterns and include noisy data in its analyses. 

What Is AIOps?

AIOps is the field where artificial intelligence meets IT operations. It uses various machine learning techniques to automate common IT tasks like anomaly detection, software maintenance, and event correlation. 

What Is Gradient Descent? 

There are various algorithms that are used to train machine learning algorithms and neural networks. Gradient descent is one such machine learning algorithm, often implemented for optimization purposes. 

Gradient descent carries out the optimization process by finding the local minima or maxima of a function. Whenever the direction of movement is towards the negative side, it outputs the local minima for a given function. It generates the local maxima when that direction is reversed, such as when the movement is towards the positive gradient. 

What Steps Would You Take To Evaluate the Effectiveness of a Machine Learning Model?

Here are the steps you can follow to evaluate the effectiveness of a machine learning model: 

Define Objectives

The purpose of your machine learning model is to solve a business problem. Start this process by studying the problem and coming up with the objectives that you have for developing a specific model. 

Determine Your Metrics

You can’t measure the effectiveness of a model if you don’t have metrics to measure it by. You should choose your metrics based on the objectives of your model. For example, if the goal of your model is to classify a certain dataset, then you would choose metrics that measure the classification algorithm’s accuracy. 

Group Your Data

You need to have three kinds of datasets in order for your model to work. The first is the training dataset, on which you train your data. Then, you have the validation dataset, which will be used to validate your results. Finally, the testing dataset is the one you use to assess the final performance of the model. 

Train Your Model

This is the stage at which you train your dataset. You can use different models to do your training and pick the one with the best performance.

Test the Model

You can use your testing dataset to test the performance of your model now. The metrics that you’ve chosen earlier in the process will come into play here. You can then compare the actual model parameters to the target metrics that you had determined. 

For example, let’s say you’re working on a model that needs to solve a linear regression problem. In that case, you would use linear regression metrics like mean squared error and mean absolute error to test the efficacy of the model. 

Interpret and Document Results

Finally, you should go over the entire process to interpret the results. That means analyzing whether any errors could have entered the model and looking for signs of biases. Once that’s done, you should document your findings so that you can communicate them to other stakeholders in the project. 

What Would Do if the Data in a Data Set Was Missing or Corrupted?

There are a few options that you have at your disposal if you have missing or corrupted data items. This includes: 

• Deleting the row or column with missing items if it’s a dataset that has enough samples 
• Using the available values to come up with an aggregate to fill in the missing values 
• Predicting the missing value using a learning algorithm 

Artificial Intelligence Interview Questions: General AI Interest

Artificial intelligence interviews often include questions that explore a job seeker’s interest in the field. These are questions that delve outside the technical aspects of AI and go deeper into exploring the passion one has for this field of work. 

What Made You Opt for a Career in AI?

This is a question that allows you to give an honest account of what got you interested in the field of artificial intelligence. Your answer should cover three main stages of your journey. This includes: 

• Initial Interest: Start by describing what sparked your interest in AI. This will likely be a description of how you saw something that was made possible by AI and how that led you to discover the underlying technology. 
• Skill Acquisition: Go on to explain how you went from being a fascinated outsider to someone who had actual AI skills. You can mention the different resources and learning methods that you used during this time. 
• Skill Implementation: You should always include some information on the AI projects that you’ve built. This shows that you’ve seen your interest through to a stage where you not only have skills but can also use them to build software. 

What Are the Ways You Continue To Improve Your Skills Related to AI?

Recruiters like to know that candidates are in the process of constant improvement. Your answer to this question can include concise information on some of the ways you pick up new skills in AI. You can mention some of the bootcamps, artificial intelligence courses, and blogs that you use in that process as part of your answer. 

What Programming Languages Are You Familiar With?

Simply mention the programming languages that you have a strong practical grasp of. You can also describe any projects that you’ve built in the languages that you include in your answer. 

What Is the Most Challenging Project You’ve Worked On? 

A common way to go about answering this question is by using the “STAR” acronym. Here are the parts that constitute it:

Situation

Describe the setting in which you created this project. Was it a personal project or was it something you did for a company? Why did you choose this particular project? What were the main goals? Answer all these questions initially. 

Task

Then, describe your role on the project. If it was a personal project, then you would’ve handled all of the work. But if you were on a team, you would have been assigned a specific part of the project. Talk about some of the problems that you were given to solve as part of your responsibilities. 

Action

Describe how you went about completing the task that you were given. Mention your initial reading of the problem and how you planned to solve it. Share any unforeseen problems that arose along the way and the means that you used to resolve them. 

Result

Cap off your answer by talking about the final product. You should focus on your contribution to the final software and, if possible, mention metrics that you used to determine that the software that you built adequately addressed the main problem statement. 

What’s the Last AI-Related Research Paper You Read? What Were Your Thoughts?

Here’s a pattern that you can use to read AI papers effectively. 

Select a Topic

AI is a field within which there are multiple subdisciplines. Ideally, you would choose a paper from an area that you are currently interested in. Let’s say you’ve been working on an image recognition project lately. In that case, you would go about looking for papers that deal with that topic. 

Do a Quick Once-Over

It’s best not to read a research paper like you read a book. Rather, start by giving your chosen research paper a first pass to gain some basic context. Your main focus will be to read the abstract and conclusion so you can figure out what the main goals of the paper are. 

Do a Deep Reading

Once you have basic context, you can go into reading the paper in depth. This is when you read from section to section and find out the methodology used to do the experimental aspects of the research. Pay special attention to any graphs and tables that visualize the data in the paper. 

Summarize the Paper

The final step in this process is to write a small summary of the paper yourself. This is the stage at which you reflect on what you’ve read and try to re-articulate the goals and main findings of the work. This will show you if there are any gaps in your understanding of the paper and help you reconstruct its conclusions when you have to talk about it in an interview. 

What Are Your Favorite Resources To Stay Up to Date With AI?

There are many ways for you to keep up with what’s going on in AI. We’ve put together a collection of the best machine learning resources here. You can find other resources with a simple Web search. 

Make sure that you actually have a good understanding of the resources that you mention as part of your answer. For example, let’s say that you mention someone you follow on social media for AI information. You should be able to describe this person’s background in AI, any significant work that they’ve done, and the kind of things that they talk about on social media. 

Do You Have Any Personal or Side Projects? Tell Me About Them.

It’s a good idea to have a few personal projects in your portfolio. These can be simple pieces of software that achieve basic AI functions. The main idea is that you have executable software that you can show and code documented on a website like Github. When talking about a project, describe its goals, programming languages and tools that you used, and the final result. 

Where Do You Usually Source Your Datasets?

Anyone who has worked on machine learning projects and similar technologies has a few go-to resources for datasets. If you’re looking for some yourself, you can check out this list of free datasets that we put together here. 

Artificial Intelligence Interview Questions: About Yourself

You will have to tackle questions about yourself and your background in your AI interview. This might happen during the HR round if it’s a multi-stage interview. You might also have technical recruiters ask some personal questions that deal with your soft skills and professional goals. 

Tell Me About Yourself.

You will have to include some personal details about yourself in this answer. But make sure to keep them concise and professional. You can start by talking about your personal background in terms of where you’re from and your educational path. As much as possible, try to talk about what got you interested in AI, what work you’ve done in the field, and what your aspirations are. 

Situational Questions Based on Your Resume.

Here are a few situational questions you can be asked based on information that you’ve mentioned in your resume. 

Why Did You Choose To Study X?

This is a question that often comes up if you come from an educational background outside of computer science. You can be honest about why you initially pursued a different field and explain how you’ve picked up skills in AI outside of an academic context. 

Do You Have Enough Relevant Experience for This Role?

You can expect this question to come your way if you don’t have a lot of core AI experience. The key to answering this in a convincing way is to cite any experience that you have with programming languages, tools, and methodologies that are relevant to AI. Let’s say that you’ve worked with Python, but outside of AI. You can let the recruiter know that your experience with the language can help you in your work as a software engineer in the AI space. 

Why Is There a Gap in Your Resume?

You might have gaps in your professional life for various reasons. You can let recruiters know the reason for them in an honest fashion and talk about why you feel motivated to get on with your career at this stage. 

What Draws You Towards This Role and Company?

You need to do some research on the company and role to answer this question. Make sure to go through the company’s website and LinkedIn so that you can pick aspects of the organization that suit your goals. Also, read the job description in depth and use that to describe how the role can help you achieve your professional goals. 

Tell Me About a Time You Made a Mistake at Work.

There is a tendency to avoid this question or try to answer it with as little detail as possible. But it’s completely okay to be honest about how you messed up in a particular situation at work. What’s important is that you also include information on how you addressed that mistake and what you learned from it. 

Can You Tell Me Your Salary Expectations?

Try to refrain from naming a specific number right away while answering this question. What you’re trying to do is show recruiters that you’re aware of market rates and that you’re willing to negotiate. Let the recruiter know that you are looking to be paid something that is in line with your experience and qualifications. You can also mention a salary range based on the research that you’ve done. 

What Are Your Greatest Strengths and Weaknesses?

This is a question that gives you an opportunity to talk about some of your soft skills. Make sure that you mention strengths that are relevant to working in AI, such as critical thinking, problem-solving, adaptability, and creativity. 

It can also be a good idea to mention any weaknesses that you’ve identified in your working style. Talk about some of the things that you are doing to ameliorate these shortcomings. 

Do You Have Any Questions for Us?

Recruiters like it when job seekers have a question for them. It shows that you’ve thought about the role in a deep manner and have clarifications that can help you understand it better. 

The questions you can ask should arise naturally as you do your research on the company and the role. Here are a few examples. 

• What would my workday look like if I’m hired for this role?
• What kind of opportunities for growth are available at this company?
• Are there specific qualities that I need to possess so I can fit the company’s culture?
• Based on my resume, are there any additional skills that I can pick up so that I can perform well in this role?

How Can You Prepare for an AI Interview?

Here are a few tips to be at your most confident, impressive best when you go in for an AI interview. 

Prepare for Standard Questions

We’ve gone over all of the standard interview questions that you can expect to encounter in an AI interview. Always make sure that you give yourself ample time to prepare for all of these questions in the run-up to your interviews. Don’t forget that the questions pertaining to soft skills and personal strength can be just as important as the technical questions. 

Do Your Research

It’s easy to overlook this part of your prep if you’re doing a lot of interviews. But regardless of how many companies you’re interviewing with, always make sure to do your research on the organization. At the bare minimum, you should have an understanding of the company’s core business, its products, and its position in the industry. You can glean a lot of the information from the company website and LinkedIn. 

Do Mock Interviews

It isn’t always easy to figure out whether you have the right body language or are able to provide succinct answers during interviews. A great way to figure this out is by doing mock interviews with a family member or friend who wants to help. You can record these practice sessions so that you can go back to the tape and see what adjustments you need to make in the way you deliver your answers. 

Prepare for a Remote Interview

Nowadays, a lot of interviews tend to happen online. It’s essential that you test all of the equipment that you will use during a remote interview. That means that you have the correct software installed, test the cameras and mic, and set up your room so that it is presentable. Also, consider investing in some quality lighting and an external if your computer’s in-built components aren’t doing the job. 

Tips To Pass the Coding Challenge

More often than not, you will have to complete a coding challenge as part of your AI interview. Here are a few things to keep in mind so that you can succeed at this step. 

Pick Your Strongest Programming Language

You might want to show off the breadth of your knowledge of different programming languages, but that might not always translate to well-written code. It’s more beneficial for candidates to pick the language that they are most confident with and complete the challenge in that language. 

Practice, Practice, Practice

Even if you’re a very good programmer, the pressure can get to you during the coding challenges. The easiest way to combat the stress of the situation is to put yourself in a simulated environment. You can pick practice coding challenges and complete them in a timed simulation so that you get the hang of producing code in this situation. 

Don’t Forget About Presentation

A lot of coding challenges in AI interviews these days involve explaining your code, so you need to spend some time practicing how to present your code. You should be comfortable talking about the work that you’ve done and fielding any questions that recruiters might have about it. 

Cracking the AI Interview and Landing a Job: Real-Life Examples To Inspire You

Getting into the field of AI and dealing with interviews can seem like an intimidating proposition. It’s always comforting to know that there are others out there who have gone through the same challenges and come out victorious. Here are a few stories that you can turn to for inspiration. 

Bharathi Priyaa

Bharathi Priyaa is an AI engineer with such illustrious names as Meta, Roblox, and Airbnb on their resume. In this post, they go over their personal experience of interviewing to work at these tech giants. There are some great first-hand accounts in there about what you can expect in AI interviews and how to prepare for different rounds. 

Alberto Romero

Alberto Romero was able to land a job at an AI startup after just four months of preparation. That might not be a realistic timeline for everyone, but you can use this post to figure out the roadmap that you can use to quickly pick up key skills and information that you need to crack an AI interview. 

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