38 Interview Questions for Machine Learning Engineers

Machine learning (ML) is an artificial intelligence (AI) subset that allows computer systems to process large datasets to perform tasks they aren’t explicitly programmed for. It’s transforming industry sectors from marketing to health care, finance, and many more. From drug discovery to algorithmic trading, machine learning powers many processes behind the scenes.

Machine learning can be a complex field requiring a blend of technical and soft skills. Machine learning engineers have to be familiar with math and statistics, specifically linear algebra, calculus, and probability theory. They should also be proficient in different programming languages like R and Python, and have in-depth knowledge of algorithms, data structures, models, and version control systems.

When recruiting machine learning engineers, make sure they have the right skills to deliver high-quality work by using a specialized set of questions to assess their knowledge and expertise.

In this article, we outline the best assessment questions you can ask machine learning engineers and provide tips to help you identify good answers.

Table of contents:

• 38 machine learning engineer interview questions
• Evaluating machine learning interview answers
• Wrapping up the interview

38 machine learning engineer interview questions

From asking about supervised and unsupervised learning to machine bias, these are the top machine learning engineer interview questions to ask and what makes for good answers to each.

1. Explain supervised versus unsupervised learning

Machine learning algorithms use supervised or unsupervised learning for training. These methods allow computer systems to identify patterns and relationships in datasets to perform specific tasks. You should ask this question to assess the person’s fundamental knowledge. Since training is a crucial part of AI systems, machine learning (ML) engineers should know how it’s done.

A good answer could be: Supervised learning involves using labeled data to train machine learning algorithms. Unsupervised learning entails using unlabelled datasets, allowing algorithms to identify hidden patterns without human assistance.

2. Explain the bias-variance trade-off

Asking about the bias-variance trade-off helps you gauge the person’s understanding of model complexity and hyperparameter tuning. It can also help you assess how prospective ML engineers will reduce bias and variance to create more accurate algorithms.

Listen for a response like: A bias-variance trade-off is all about finding a balance between errors that result from bias and those from high variance. This can minimize errors and develop reliable algorithms.

3. How would you deal with missing or corrupted data in a dataset?

Machine learning involves processing and analyzing large datasets. So, missing and corrupted data can impact the training process and even create issues like false negatives and false positives. This question will help you assess the engineer’s data science skills in cleaning and preparing data for training.

Responses like filling in missing entries with calculated values and removing corrupted entries may show a person’s proficiency in cleaning datasets. Other good answers can include using algorithms that account for missing data or data augmentation to generate missing data from existing information.

4. What is cross-validation, and why is it useful?

A question about cross-validation allows you to know how a person will evaluate model performance. You want to create high-quality algorithms that perform intended tasks, and this starts by finding people who know how to monitor model performance during training.

A good answer can be: Cross-validation is a statistical method for evaluating model performance. It involves breaking down large datasets into multiple categories and using them for training and evaluation. It’s useful because it reduces instances of overfitting and improves the model’s ability to perform well on any new data.

5. Describe a time when you had to implement regularization in a model

Regularization is a technique for reducing overfitting in machine learning. In this context, overfitting is a situation where machine learning models provide accurate responses for training data but fail to do so when exposed to new data. This question can help you determine how people handle the ML overfitting issue to build reliable models.

Look for answers like: I implemented regularization when the model performed well on training data but poorly when exposed to new information.

6. How would you evaluate a machine learning model’s performance?

Machine learning engineers don’t just create new algorithms—they also need to confirm that models can work effectively. So, this interview question will give insight into how people assess model performance.

A good answer can be: I could evaluate a model’s performance by using cross-validation. This method involves dividing the data into multiple subsets, which are then rotated to serve as training datasets and test data. After training, I would analyze the model’s accuracy using the test set. Consistently high scores indicate good model performance.

7. Describe L1 versus L2 regularization

Both L1 and L2 regularization are methods for dealing with overfitting in machine learning. This question lets you gauge ML engineers’ understanding of the differences between the two techniques—and if they can pinpoint which to use in specific cases.

An example of a suitable answer is: L1 regularization works by adding the sum of the absolute values of the model’s coefficients as a penalty to the loss function, while L2 regularization adds the sum of the coefficients’ squares. This helps control overfitting by penalizing large coefficients.

8. What’s the difference between batch gradient descent and stochastic gradient descent?

Gradient descent is an algorithm that helps reduce errors between predicted and actual outputs, resulting in more accurate and reliable techniques. Asking this question allows you to confirm that the person knows the different gradient descent methods and when to apply them.

A satisfactory answer can be: Batch gradient descent involves computing the cost function’s gradient using the whole training dataset in each run, which is quite resource-intensive and slow. Stochastic gradient descent updates the model’s parameters using just one sample or a small batch of samples at a time, making it more efficient, especially when processing large datasets.

9. How do you handle overfitting?

Overfitting occurs when a model doesn’t perform as expected when exposed to a new dataset. This question will help you assess a person’s problem-solving skills.

Listen for answers like: I would use regularization to reduce cases of overfitting in machine learning projects. This may involve using specific regulation techniques like bias-variance trade-offs or L1 or L2 regularization to minimize errors. Also, using a diverse dataset born from data augmentation for training can help reduce instances of overfitting.

10. Explain principal component analysis (PCA) and when you would use it

ML algorithms rely on large datasets for training, but this data may not always contain suitable attributes. Asking about principal component analysis lets you determine how ML engineers will deal with highly dimensional datasets and convert them into more usable formats.

A good answer can be: Principal component analysis is a dimensionality reduction technique that also keeps essential features and variables. It’s useful in data preprocessing, noise reduction, and visualization tasks.

11. Describe how a decision tree works. What are its advantages and disadvantages?

A decision tree is a supervised learning method for regression and classification activities. ML engineers with a mastery of decision tree fundamentals can create effective algorithms to identify intricate patterns and relationships in big data.

Here’s an example of a good response: A decision tree provides a structure that determines the flow of decisions or information, with decisions made from the root node to the “branches.” A decision tree requires less data cleaning and is suitable for handling problems with multiple outputs. However, noise can affect a decision tree and make it more prone to overfitting in large datasets. Constraints like pruning or setting a maximum depth can help.

12. How do random forests work, and how are they different from decision trees?

Like the decision tree, a random forest is also a learning technique for regression models and classification tasks. So, ML engineers must understand how it works and how it differs from the decision tree.

A correct response should flow as follows: A random forest is an algorithm consisting of multiple decision trees. But rather than relying on a single node for prediction, a random forest combines outputs from multiple decision trees, resulting in a more accurate response. Random forests are more flexible and effective at reducing overfitting, but they can consume more resources when processing large datasets.

13. Explain the core concepts behind support vector machines

A support vector machine (SVM) is a common algorithm for dealing with regression, classification, and outlier problems. ML engineers have to know what SVMs are, the core features and concepts behind them, and how they work. This question can help you assess their familiarity with support vector machines.

Good responses should be something like: SVMs are supervised ML algorithms that boost a model’s prediction ability by reducing overfitting, especially when dealing with large, highly dimensional datasets. Core concepts behind SVMs include data splitting, hyperparameter tuning, and optimal hyperplane selection, all of which contribute to more accurate prediction and classification.

14. How do neural networks work? Can you describe backpropagation in simple terms?

Neural networks are aspects of machine learning that facilitate the analysis of intricate patterns and relationships in data but at a much deeper level, resulting in more accurate systems. So, this question can help you identify the right ML engineers who are knowledgeable about neural networks and deep learning. Asking about backpropagation lets you assess how ML engineers deal with errors while working with neural networks.

Suitable answers should sound like: Neural networks use interconnected nodes grouped into layers to process large amounts of data and identify hidden trends, insights, and relationships. This is how it uses training data to perform complex tasks. Backpropagation helps you train and fine-tune neural networks—but in this case, you do it backward using loss functions, which makes neural networks more accurate.

15. What are the differences between recurrent neural networks (RNNs) and convolutional neural networks (CNNs)?

Different neural networks have different pros and cons, and qualified ML engineers should be able to identify them, including their core features, advantages, disadvantages, and areas of distinction. This question tells you if an ML engineer knows about the different types of neural networks and when to apply them.

Successful interviewees should say something like: Recurrent neural networks handle and generate textual data, making them useful in generative AI apps. For example, they can process articles and even generate entirely new sentences based on the training data. Convolutional neural networks are effective at processing grid-like data such as images, which makes them effective in computer vision applications.

16. Explain how dropout is used in neural networks

Neural networks are subject to overfitting, which can affect output quality. Asking about dropout allows you to evaluate how workers can apply regularization to make models accurate even when processing new data.

A correct answer is: Dropout is a regularization technique that helps to reduce overfitting. In neural networks, it deactivates specific nodes in input and hidden layers, resulting in a more direct structure.

17. What are generative adversarial networks (GANs) and their applications?

Generative adversarial networks are at the heart of generative AI applications. This question can help you assess a person’s understanding of the generative AI field and its applications.

Example of a correct response: Generative adversarial networks are deep learning networks capable of producing new forms of data based on their training data. GANs support many applications, including image and text generation, synthesizing realistic human voices, creating new video content, and generating virtual environments.

18. Describe how the k-means clustering algorithm works

Asking about the k-means clustering algorithm helps you gauge ML engineers’ familiarity with clustering methods in machine learning.

A possible answer can be: The k-means clustering algorithm helps categorize unlabeled datasets into multiple clusters with similar properties by calculating the distance between various data points and a centroid and using the results to determine which cluster they belong to. This grants you access to clusters with meaningful information for training purposes.

19. How do you choose the number of clusters for a clustering algorithm?

Clustering lets you analyze unlabeled datasets and place them into multiple groups based on shared characteristics. However, you must know the number of clusters in advance for effective clustering. This question can demonstrate the interviewee’s mastery of clustering techniques, system design concepts, and algorithms.

An example of a satisfactory response is: You can determine the number of clusters for a clustering algorithm using techniques like the elbow method, gap statistics, or the silhouette method.

20. Describe the differences between bagging and boosting

Ensemble learning is an essential aspect of machine learning that improves model performance and accuracy, particularly when making predictions. Bagging and boosting are essential concepts in ensemble learning. This question can help you gauge how interviewees will use ensemble learning techniques to boost quality.

A right answer should sound like this: Bagging helps minimize errors resulting in a model’s variance while boosting decreases bias during training. Weak learners are trained in parallel in bagging, while the boosting technique is done sequentially.

21. Explain how gradient boosting works

Bias can impact AI performance and affect output quality. This question lets you determine how people use gradient boosting to minimize bias and boost reliability.

A good response can be: Gradient boosting helps reduce bias. It works by adding predictors or weak learners to an ensemble and training them based on errors of the previous predictor.

22. What are the key differences between parametric and non-parametric methods?

Machine learning involves analyzing and processing large amounts of information. So, ML engineers should be familiar with parametric and non-parametric methods, including their differences and similarities, to extract meaningful insights from data.

A satisfactory answer would be: Parametric methods rely on different assumptions, including that data follows a specific distribution. These assumptions make the methods less flexible and can potentially affect model accuracy. Non-parametric methods do not rely on assumptions but rather random sampling, which allows them to fit into numerous applications.

23. How do you address class imbalance in a dataset?

Class imbalance occurs when certain classes appear more or less than others. This leads to bias and ultimately affects data quality. ML engineers should use their data engineering skills to address class imbalance and create more robust AI algorithms.

A possible answer can be: You can use the gradient boosting method to minimize bias in the training dataset. Resampling techniques can also help correct minority or majority instances. Or, you can use data augmentation to create new balanced datasets based on existing information.

24. Describe a scenario where you would use a Naive Bayes classifier

Asking about the Naive Bayes classifier helps you gauge the person’s understanding of the algorithm and where, specifically, it fits in the machine learning workflow.

An example of a correct response is: I would use a Naive Bayes classifier to analyze social media data and classify it based on positive, negative, or neutral sentiments.

25. How does collaborative filtering work in recommendation systems?

Recommendation systems provide suggestions based on individual preferences and behavior. The addition of collaborative filtering makes these systems more robust and capable of providing interesting recommendations.  For example, Amazon uses collaborative filtering on its website to provide interesting recommendations to users.

Qualified ML engineers should provide an answer like: In recommendation systems, collaborative filtering works by analyzing other users’ (who have similar traits) interests and using the information to provide recommendations.

26. What is an embedding layer in neural networks? When might you use one?

The embedding layer is a fundamental aspect when creating neural networks. It’s useful in converting data types into suitable formats that can be processed effectively. This concept is particularly useful when creating natural language processing (NLP) applications because AI systems can transform words into continuous vectors for analysis. This question will help you assess a person’s familiarity with data representation techniques in deep learning and where they can apply them.

An example of a good answer might be: The embedding layer is an architecture that helps transform data inputs like words into continuous vectors. It works in NLP applications such as question answering, information retrieval, and text classification systems.

27. How do reinforcement learning algorithms function? Give an application example

Reinforcement learning is a popular way of training AI systems to perform specific tasks. Experienced ML engineers should know how these algorithms work to create robust AI applications when needed.

A good response should sound like this: Reinforcement learning algorithms function by interacting with various agents in a dynamic environment. These models update their knowledge bases with the positive or negative feedback generated after performing specific tasks.

28. Explain the concept of word embeddings in NLP

Word embeddings are essential for understanding the relationships between words by representing them as dense vectors in a high-dimensional space, capturing semantic and syntactic similarities. A solid grasp of word embeddings is fundamental for assessing proficiency in NLP and its various applications, as it forms the basis for more advanced techniques and models used in the field.

A possible answer is: Word embeddings are a method of representing words as vectors in a multidimensional space. These concepts allow NLP apps to easily identify relationships between words and thus improve their ability to process natural language.

29. Describe how attention mechanisms work in neural networks

Asking about attention mechanisms helps assess a person’s familiarity with different optimization techniques in neural networks. The question also tells you whether or not they know how AI systems keep track of context and how they can enhance their performance and accuracy.

ML engineers should answer like this: Attention mechanisms assign different weights to words in a sentence. As a result, computer systems can focus more on inputs with higher weight assignments, capturing context in the process.

30. How would you approach a time series forecasting problem?

Time series forecasting involves predicting future states based on sequential data. ML engineers knowledgeable with time series forecasting fundamentals can solve different prediction-related problems.

An example of a good response is: First, I would analyze the forecasting problem and the training data. I would then clean the datasets, handling missing data and outliers. Then, I would find a suitable model for data analysis. Finally, I would train the model and evaluate the results.

31. Explain the difference between a loss function and a metric

Loss function and metrics are essential ML concepts that apply to different use cases. This question shows that an ML engineer understands machine learning fundamentals, including minimizing loss during training and evaluating results.

A suitable response should be similar to: A loss function is used during training to determine the variation between expected and predicted model outputs. A performance metric is used in model evaluation to determine how it performs desired tasks. F1 score can also be used as an evaluation metric for precision and recall (model quality attributes).

32. How do you determine feature importance in a model?

Different features can impact model performance and accuracy. The more important a feature is, the greater its impact. ML engineers must know how to determine feature importance to build reliable and high-performing models.

Good response example: I calculate feature importance using the Gini importance technique to determine how each feature minimizes uncertainty.

33. What steps would you take to deploy a machine learning model into production?

Multiple steps should be followed to push a model to production successfully. As a result, you should look for people who are experienced with deploying ML frameworks in different production environments.

Look for answers like: I would identify the production environment and research its requirements. Next, I would containerize the model, including all the necessary plugins and tools like pandas. I could then push the model to the production environment and monitor its performance.

34. Describe the challenges and considerations of deploying a deep learning model in real-time applications

Deep learning models can bring many workflow benefits, but they can also introduce challenges that impact productivity. You need experienced ML engineers who can deal with challenges like these as they arise.

Qualified ML engineers should answer as follows: Some challenges when deploying deep learning models in real-time apps include compatibility issues, data privacy, resource limitations, and inadequate skills to handle the models. Before deploying a model, I’d make sure it’s compatible with the existing tech stack and that the team has the skills to use the model.

35. How do you monitor and maintain a model once it’s in production?

Once deployed, models still need to be maintained for continued performance. This means ML engineers need to know about continuous improvement and development initiatives.

A good answer would be: I would use machine learning evaluation metrics to monitor model performance by calculating accuracy and mean scores.

36. How do ensemble techniques work, and why are they beneficial?

Ensemble techniques are key to creating quality AI systems. ML engineers should demonstrate good mastery of these concepts to enhance the performance of AI models.

Correct responses should sound like this: Ensemble techniques let users combine multiple models into a single, high-performance system. They help lower the margin of error, resulting in more accurate systems.

37. Describe any experience you have with transfer learning

This question helps you determine if the person is familiar with transfer learning and has applied it in previous projects.

Possible answer: I have used transfer learning to build computer vision apps for image classification. Transfer learning allowed the underlying model to apply capabilities gained from performing other tasks to complete new activities.

38. How do you handle situations where the model’s performance starts to degrade over time?

Model performance can degrade over time. ML engineers should know how to optimize these models to perform as required.

Correct answer: I would handle a situation where a model’s performance degrades over time through retraining. This may involve using more diverse and larger datasets to fine-tune the model performance.

Evaluating machine learning engineer interview answers

While performing job interviews, you’ll meet exceptional ML engineers and data scientists with the right technical skills and knowledge. How do you set the best of them apart from the rest? Next, we’ll review some aspects to look for during machine learning interviews.

Looking beyond the technical

Machine learning is highly complex, but ML engineers still need certain soft skills to thrive. Excellent communication, collaboration, problem-solving, and interpersonal skills will help them work well with others and become a valuable team member.

Also, look for signs of adaptability and a growth mindset. Machine learning is rapidly evolving, and engineers must be flexible and adaptable to keep up with changing trends and situations. A growth mindset means they can learn, sharpen their skills and competencies, and ultimately improve in their roles.

Red flags to watch for

While asking ML engineers interview questions, tech companies should watch for the following red flags:

• Over-reliance on jargon without understanding
• Inability to communicate complex ideas simply
• Limited understanding of ML-centric programming languages like Python
• Not wanting to communicate or collaborate
• Negative attitude, including rudeness, ignorance, and laziness
• Poor data handling and SQL skills
• Inability to take responsibility
• Being late for interviews

Green flags for hiring

When interviewing ML engineers, the following green flags may indicate that a person can thrive in the role:

• Good fit of expertise and soft skills
• Ability to take responsibility
• Familiarity with data science and software engineering fundamentals
• Willingness to learn
• Good time management
• Willingness to accept change
• Readiness to communicate with and assist others
• Positive response to feedback and criticism

Wrapping up the interview

Once you’re done asking questions, wrap up the interview process by summarizing key insights and assessing the person’s enthusiasm. Let the applicant ask questions and give them clarification as needed.

While wrapping up, you can also share next steps. For example, if you haven’t made a hiring decision yet, let the interviewees know how and when you’ll communicate it.

Find machine learning experts on Upwork

Machine learning powers computer systems and apps that can autonomously and accurately perform mundane and repetitive tasks. These applications can enhance productivity, boost cost-effectiveness, and streamline numerous workflows. The above questions and answers can help you bring in the right talent for your role.

However, creating machine learning algorithms requires a blend of technical and soft skills, which may take time for beginners to gain. With the high demand for engineers, finding ML experts to fill full-time positions can also be time-consuming for hiring managers. Consider working with freelance machine learning experts on Upwork to get your AI projects done quickly.