So, you’ve decided to take the leap into the world of machine learning engineering—great choice! But let’s be real, ML interviews can feel like decoding an alien language. Whether it’s algorithms, data structures, or real-world problem-solving, you need to be prepped and ready. Don’t worry; we’ve got your back! In this guide, we’ll walk you through the top ML engineer interview questions for 2025, helping you crack the code with confidence. From technical deep dives to practical scenarios, let’s get you interview-ready like a pro!
Practise these questions and follow the preparation tips to land your dream job as a machine learning engineer. Reason? The AI and machine learning job market has exploded in 2024 and is expected to grow by 47% by 2027, increasing interview competition.
According to ML specialists, your hands-on experience in Machine Learning attracts technical recruiters. If you review multiple machine learning interview experiences, you will notice common patterns in the questions asked. In this article, we will discuss that pattern in greater detail. Check out this X trend below!
Prepare for your Machine Learning interview with a fun twist! Here are the Top 5 ML Interview Questions that will make you the star candidate. Swipe through for insights and a good laugh! 😄💡
👇 Drop your own fun analogies in the comments!#MachineLearning #InterviewPrep pic.twitter.com/w6rwvkat4l
— Ria (@rialogy_ai) June 18, 2024
Types of ML Engineer Interview Questions: You can’t afford to miss
Project-Specific Questions: Beginning of the Funnel. Questions related to your project and data handling.
Generic Questions: A bucket of basic knowledge in ML. The interviewer asks these to assess your base in ML
Let’s discuss the project-specific questions first:
Project-Specific ML Engineer Interview Questions:
The interviewer will assess your experience with past projects from your career or coursework. You may be asked this specific question about the project’s goals, including why it was started and what you have accomplished.
Q-1: Can you describe your recent ML project?
This is one of the common ML engineer interview questions. Focus on the following key points to frame your answer:
- Select a project: Choose a project to showcase your technical expertise; make sure it aligns with the firm you are applying to.
- Briefly explain your role and contribution: Explain how you are involved with the project; as a team member, what did you deliver as a part of the team? This must include your contribution, such as the instruments, methods, models, or algorithms you employed.
- Challenges and Learnings: Discuss any challenges or issues that you faced during the project and how you encountered them. Explain your learnings.
- Outcome and Impact: Explain the results of the project. Make sure your answer is directed to the expectations of the interviewee. Consider framing your answer on the following pillars:
- Did it achieve the goal?
- How was your organisation impacted by the project?
- While discussing outcomes, focus more on the methods and techniques that you have used and how your data manipulation helped you to drive the desired results.
Here is a template to help you prepare your answer:
Answer template: In my last role as “Mention Post” at “ABC Company,” I worked on a live machine learning project that aimed to “describe the aim of the project” using “describe the methodology used in the project.” My responsibilities included “xyz; focus on the languages you have used in the project.”
During the project, I encountered major issues. “Explain the issues you encountered, but I managed to resolve issues by “focusing on the initiatives you have taken to resolve the encountered issues.”
By the end of the project, the model achieved an accuracy of (X%), demonstrating its success.
Q-2: Explain the pipeline of your project.
Keynote: It is the most important part of the interview, and your answer may be viewed as the beginning of the conversation. In your answer, the interviewer may focus on the steps described in your pipeline. So, one must be clear and concise while explaining the steps so that the interviewer can anticipate your knowledge and move forward with the conversation.
Here is a blueprint to help you prepare your answer:
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- Define your pipeline stages as components: Be confident and explain your pipeline stages as components; try to arrange your execution logic on the following points:
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- Define the component interface (each component’s input and output).
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- Add additional metadata about the component, such as the run-time environment and the command to launch the component.
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Template: I/we used [mention the dataset] for this project to (mention the aim of your project). I collected/used (mention your initiatives or tools that you have employed) and created (mention framework) to (specify how that framework helped you in your project). Using (mention the outcome of your project), I/we built a pipeline to (explain how it helped the project).
Q-3: What was the accuracy of your model?
This ML engineer interview question assesses your confidence in discussing your project. Use this opportunity to highlight your model’s quality. You must state the goal you aimed to attain as well as the results.
Template: With (mention algorithm, you have used), I/we achieved (mention%) of the model; the reference model we used claimed to have xyz% of accuracy in the training set.
Q-4. How do you ensure the scalability and performance of your machine-learning models?
Scalability and performance reflect your ability to maintain and improve machine learning infrastructure to meet organisational needs.
Answer Hints:
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- Mention containerisation tools like Docker and Kubernetes for scaling applications.
- Frame your answer w.r.t:
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- Handling large datasets
- Features
- Framework
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Template: For large volumes of data, I/we will consider (mention the optimisation technique/framework/feature you will use) and parallelise processes like (explain how the technique/framework/feature will give you the desired result).
Pro tip: Ensure that your CV highlights the project’s goals, outcomes, and achievements.
ML Engineer Interview Questions Based on Data Handling
Q-1. What percentage of data was used to train the machine learning model?
The answer to this question is quite simple. However, if you ask any data scientist how much data is required for ML, he will most likely say, “It depends” or “the more” is “the better.” And the truth is, either response is right.
Frame your answer by specifying the percentage of data used for each subset.
Template: I/we split the datasets into training (X%), validation (Y%), and testing (Z%) sets, achieving an accuracy of (X%).
Q-2. Explain how data should be preprocessed before being fed into a machine learning model.
Creating usable raw data for machine learning involves several steps:
- Data cleaning: Involves filling in missing values, fixing or removing outliers (unusual values), and resolving inconsistencies in data.
- Data transformation: To ensure that the model is not excessively impacted by the extent of various features, this aims to normalise or scale data features.
- Features selection: Eliminates features that are superfluous, redundant, or highly correlated.
- Feature engineering: The process of creating additional features from pre-existing data to increase the diversity and accuracy of the model.
- Data Encoding: Converts categorical data into numerical data using techniques like one-hot encoding or label encoding.
- Data Splitting: To assess the model’s performance, separate the data into training and testing sets.
Q-3: How do you handle missing or corrupted data in a data set?
This question evaluates your problem-solving skills and experience with raw data.
Tips to answer: Explain what factors you consider while evaluating different strategies for addressing missing or corrupted data. Try to involve factors like the distribution of data, underlying assumptions, computational efficiency, and the needs of the data. Focus your answer on these factors. Then stress on how these factors helped you in making an informed decision.
Focus on covering your approaches, including visualisation, statistical testing, exploratory data analysis, etc. Compile your answer, emphasising your proficiency with programming languages, tools, or libraries. Refresh your learning about statistical testing here:
Template: Addressing missing values starts with carefully identifying gaps in the data. My/our approach includes the “mention approach” to determine if the absence is random or systematic. Then, I will assess the accuracy of the data through the “mention metric you will use like MAE/MAR/MNAR,” and fix the root cause first.
I/we will employ “mention algorithms like Random Forest, Gradient Boosting, and Neural Networks.” as a proven approach to provide more accurate imputations than simple statistical methods.
Q-4: Explain how you ensure compliance with data protection regulations (like GDPR).
MLops professionals, to safeguard user data and stay out of trouble with the law, make sure that data complies with GDPR.
As the question is theory-specific, you must try to prepare your answer on the below hints:
- Mention tools like Databricks for data governance and secure enclaves for handling sensitive data.
- Discuss the importance of frequent audits to guarantee continued adherence to data protection regulations.
Answer Template: To ensure GDPR compliance, I will use:
- Data Anonymisation and Encryption secure data privacy methods to remove Personally Identifiable Information (PII) from datasets used for model testing and training.
- Audits and Access Controls to guarantee that only authorised individuals have access to sensitive data, implement strict access restrictions. Keep thorough audit records to monitor data access and modifications.
- Data Minimisation and Retention Policies adhere to the principle of data minimisation by collecting only the data necessary for specific purposes. Implement clear data retention policies to ensure data is not kept longer than necessary.
Generic Theoretical ML Engineer Interview Questions
Let’s have a look at the basic and advanced most asked theoretical questions in the machine learning interview.
Q-1: What is the difference between supervised and unsupervised learning?
In supervised learning, the models learn from labelled input-output pairs to map the relationship between them. Some popular algorithms for Supervised learning are Linear regression, Logistic Regression, Decision Trees, etc.
In unsupervised learning, machines only know the input and map functions to bring similar samples together. Some popular algorithms for Unsupervised learning are Principal Component Analysis, k-means clustering, etc.
Check out what your peers are saying about supervised and unsupervised learning.
Q-2: What is stemming, and how is it different from lemmatisation?
Stemming is a technique in data preprocessing used for text data. Using this technique, we bring words into their native form by removing common suffixes and prefixes added to the word. For example, interchanging will be converted to interchange.
There are some pros and cons related to this technique; pros: The complexity of these algorithms is less, and hence execution becomes faster. Cons: These algorithms do not preserve the true meaning of the words before slicing.
On the other hand, lemmatisation also reduces words to their native form while preserving their meaning. The complexity of these algorithms is higher; hence, it is only used when the dataset is small.
Q-3: Explain the workings of the Gradient Descent Algorithm.
Gradient descent is an optimisation technique used to optimise the cost functions in machine learning. Machines try various sets of parameters and try to find which sets of parameters produce the least cost function and store corresponding parameters in the form of learning.
GD speeds up the process of finding these sets of parameters using calculus and states that where the cost function gradient is zero, the cost will be either minimum or maximum. But we design the cost function to include only minima; hence, it drives the updates of the parameters in those portions where the gradient approaches zero.
As said, the better the reading, the better the preparation, and the higher your chance to be a potential candidate that the recruiter is looking for.
Important Top ML Engineer Interview Questions
- What is the Bayes theorem? How is it useful in machine learning?
- What is the Kernel trick, and how is it useful?
Conclusion
With a surge in ML jobs, competition is strong. So, it has become important to learn ML engineer interview questions to land your dream job. These ML interview questions can help you excel in the technical rounds of FAANG companies.
A structured approach ensures thorough preparation and boosts confidence during the selection process. Here are a few tips to gear up your success:
- Timetable and proper planning: Start preparing two months before your interview to prevent stress. Establish a reasonable study schedule that balances regular daily practice and learning with breaks. Conduct mock interviews closer to the interview date to build confidence.
- Focus on mastering key concepts in machine learning, statistics, and programming. Divide your study sessions into manageable subjects like model evaluation, supervised learning, and unsupervised learning.
The templates and the preparation tips shared in this blog will help you appear confident in your next interview. Wish success to all the ML applicants reading this post.
FAQs
Q1: How do I prepare for my ML interview?
Understand the role, research the company, and prepare for coding challenges. To strengthen your preparation by reviewing mock interview questions, read about the latest developments in ML/AI.
Q2: Can I get an ML job as a fresher?
Freshers with a background in CS, mathematics, or statistics can pursue their career in ML.
Q3: What are the 5Cs of interviewing?
The 5 Cs of interviewing are: confidence, competence, communication, character, and chemistry.
Q4: Is ML a high-paying job?
ML is a high-paying entry-level job with a compensation rate of up to INR 11 LPA.
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