How well can top LLMs solve ML Interview Questions?
Check out our paper that explores this question in detail. We are testing different top models like OpenAI's O1 on ~160 multiple-choice questions. You will be surprised by the results!
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Take me there!AI engineering interviews diverge from traditional software engineering ones, placing greater emphasis on conceptual understanding of data science and machine learning principles. A typical interview consists of a (natural language) technical discussion between the candidate and the interviewer, where the interviewer is looking after a few key competency markers with each posed question. Therefore an interactive experience is needed to properly train for such a scenario; neuraprep.com accomplished just that, providing dynamic feedback through simulated interview scenarios.
We have built the largest repository of Data and AI interview questions, featuring over 400+ meticulously curated problems along with expert solutions developed by the founders themselves. We use machine learning to compare your response against the ideal solution from our database and provide tailored feedback to help you improve and succeed.
Includes interview questions from all AI subfields, such as machine learning, data science, statistics, and more.
Learn MoreAI isn't all theory and math. Our coding questions equip you with the practical skils to tackle real AI problems.
Learn MoreEvaluate your ability to design large-scale machine learning systems. Requires specialized knowledge in software architecture.
Learn MoreWe've carefully crafted multiple quizzes to test your understanding of AI concepts, tailored by difficulty level and specific subfields.
Learn MoreMachine learning interviews demand a comprehensive understanding of the field, typically evaluated through in-depth technical discussions. Our platform replicates this interview experience by presenting questions used in real AI interviews, organized by difficulty level. This immersive simulation allows you to effectively practice and demonstrate your machine learning expertise, ensuring you are well-prepared for your next interview.
We have also meticulously curated quizzes, each typically containing seven questions, to provide quick assessments tailored by difficulty level.
With over 400 questions spanning all subfields of data science and machine learning, our platform offers the most comprehensive collection of interview questions. Those who complete every single challenge on this platform will find no AI technical interview a threat.
It's important to know more than just theory and math before walking into machine learning interviews, because the coding portion of these interviews will test your knowledge in an applied format.
Our thoughtfully selected machine learning coding interview questions encompass the most crucial applied concepts and are sourced from actual interview experiences. No boring toy problems like coding a vanilla neural network in numpy, our coding challenges leverage the most up-to-date frameworks and practices.
Designing large-scale machine learning systems goes beyond just machine learning fundamentals and requires a profound understanding of numerous specialized machine learning topics.
Proficiency in concepts such as producer/consumer architectures and Kafka streams is essential for excelling in any ML system design interview.
Check out our paper that explores this question in detail. We are testing different top models like OpenAI's O1 on ~160 multiple-choice questions. You will be surprised by the results!
Read MoreWe explore ideal answers for a few most popular questions that get asked as barrier of entry in this career.
Read MoreGet a glance at the requirements to work in two of the most influential industries in word: tech and finance. Explore a few interesting interview questions from Hedge Funds and fintech.
Read MoreThis feature is aiming to truly simulate the interview experience, being the flagship of our business. Here, if you do not know the complete answer to the question straight away, the marker won't directly grade you (like it happens in regular Premium), instead, you will receive guidance and questions that will lead you towards the answer that the interviewer wants to hear. Long story short, this is a mini conversation (of maximum 3 attempts back and forth) between you and the interviewer, where you both work towards solving the problem. This is not just using a wrapper around an LLM, the language model always refers to the golden answer from our proprietary database to really have the full context and act like an interviewer. This experience cannot be replicated on the ChatGPT website.
Coding questions are evaluated exclusively using unit tests tailored for each problem, while quizzes are graded by comparing your answers to the correct solutions. Our 'Questions' section evaluates submissions using our own proprietary ML-powered essay marking system. This system carefully compares your answers to our hand-crafted, trusted ideal responses from our database focusing on the key concepts that interviewers look for, providing accurate and meaningful assessments.
This website was built out of passion for interview assessment and helping people land their desired job. All interview question answers on our platform are expertly crafted by our two technical cofounders. With advanced degrees in AI from renowned universities and over 8 years of combined industry experience, they've developed large-scale ML systems and recommendation engines serving millions of users at leading tech companies. Their research contributions have garnered over 80 citations. Their skill set encompasses a broad range of technologies and methodologies, including Python, PyTorch, AWS, GCP, MLOps, distributed computing, and quantitative finance. One of our cofounders is a Lead Machine Learning Engineer who has interviewed over 100 candidates throughout his career, driven by a deep passion for interview assessment and a commitment to excellence.
We believe we have constructed the most comprehensive and complete list of interview questions in the data and ML space that there is to date. This is what powers our platform. We have sourced questions from open platforms such as Glassdoor, Blind, Quora, Stack Exchanges, YouTube, as well as from both our own writings and public blogs, among many other reputable sources. Every question on our platform has been used in real interviews. While some theoretical questions, like 'What is the bias variance tradeoff?' are so fundamental that specific sources may not always be identifiable, they remain essential components of data and machine learning interviews. Regarding coding questions, we understand that machine learning interviews often involve take home assignments rather than live coding sessions. As a result, not all coding questions on our platform originate from actual interviews. Instead, we have adapted shorter coding challenges to resemble the unique machine learning coding problems commonly encountered in the industry that are not typically found on platforms like leetcode.
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