Several sectors have started using machine learning (ML) and artificial intelligence (AI) over the last few years. Some examples are healthcare, retail, finance, banking, and manufacturing.
For hiring managers, this means that they’re competing across industries to source skilled ML and AI experts, which makes the task even more challenging. And finding the right talent (data scientists, machine learning engineers, etc.) has never been more important.
It’s why it’s so crucial to ask the right machine learning engineer interview questions, so you hire only the best candidates – and combine this with other methods to accurately assess candidates’ expertise and knowledge, such as skills tests.
Make your life easier by choosing a recommended skills testing platform like TestGorilla and use our Data Science and Machine Learning tests to evaluate applicants.
Candidates who perform well on these tests fully understand the fundamentals of data science and machine learning. They'll also have the necessary knowledge of neural networks, programming, statistics, and deep learning.
1. Define deep learning. How is it different from other machine learning algorithms?
Deep learning is a particular form of machine learning based on neural networks. This involves the use of neuroscience principles and backpropagation to correctly model large sets of data, both semi-structured or unlabelled.
In summary, deep learning is the mechanism by which an algorithm learns without supervision. It learns data representations through neural nets.
2. Is model accuracy or model performance more important to you?
Here, you are testing the candidate's understanding of the nuances of model performance. Generally, machine learning questions focus on details. However, more accurate models can perform worse when making predictions.
A candidate must understand the accuracy of a model is only an aspect of how well the model performs.
3. Explain how you would ensure you don’t overfit a model.
Your candidate needs to demonstrate that they understand the three key routes to not overfitting a model.
To avoid overfitting a model, a data scientist can:
Simplify the model, or remove some of the noise by reducing variance
Use cross-validation tactics, such as k-folds
Use regularization tactics, e.g., LASSO, to penalize parameters that could allow overfitting
4. What is a hash table?
A hash table is a data structure that creates an associative array. You map out a key to certain values using a hash function. Hash tables are usually used for database indexing.
5. Explain what you believe is our business's most valuable data.
With this question, you’re testing how much your candidate knows about your business model and the wider industry.
You’re also checking whether they understand how data corresponds to your business outcomes and how they will apply this knowledge in their work. Do they understand the problems that your business wants to solve with data?
6. Could you name a few machine learning papers you’ve read recently or explain how you follow the latest developments in machine learning?
The best candidates will keep abreast of the latest scientific reports on machine learning. Look for well-referenced journals, such as Nature.
7. Explain how you would reproduce AlphaGo’s approach at Go that beat Lee Sedol.
The year 2016 was important for the history of deep learning and machine learning. Then, AlphaGo, a computer program that plays Go, beat the top human Go player, Lee Sedol.
Your candidate should show they understand how AlphaGo achieved this. It utilized Monte-Carlo tree search with deep neural networks. These networks are trained through supervised learning of human games and self-play.
8. Do you think quantum computing will affect machine learning? How?
Here, you’re testing your candidate's interest in machine learning at a high level and not just their ability to implement it in specific tasks.
There have been several important quantum computing breakthroughs. Your best candidates will show an interest in the field and be able to talk about the idea that some algorithms may yield better results on quantum computers.
9. What research experience do you have in machine learning?
Candidates with published research papers can really stand out here – this demonstrates valuable scientific and academic experience.
10. What data types does JSON support?
Your candidate should show they understand the six basic JSON data types: objects, strings, arrays, booleans, numbers, and null values.
11. List some differences between an array and a linked list.
A linked list is an ordered group of elements where the elements are connected through pointers. A linked list is more likely to grow organically.
An array has to be defined for growth. An array will also assume the same for all elements, while the linked list will not. And finally, shuffling an array is complex and costly. Shuffling a linked list involves just changing the pointers.
12. Explain how you would assess a logistic regression model.
Your candidate must show a deep understanding of common logistic regression goals, such as prediction, classification, and more. Ensure they’re able to talk about use cases and examples.
13. When should you use classification over regression?
Ensure your candidate understands that regression gives continuous results while classification creates distinct value to strict categories.
You would choose classification over regression if you want the output to show that data points belong within specific categories.
14. How would you prune a decision tree?
Your candidate needs to show they understand pruning.
Pruning a decision tree refers to the process of removing branches with weak predictive power. This simplifies the model and increases predictive accuracy.
Examples are cost complexity pruning and reduced error pruning, the latter being the easiest version of pruning. In it, you prune by replacing each node, so long as it doesn't decrease predictive accuracy.
15. What’s your favorite algorithm? Give me a simple explanation.
This tests your candidate’s ability to explain technical details in layman’s terms. This is important for good communication between technical and non-technical staff.
Look for candidates who can explain different algorithms in a way that is simple and easy to understand.
16. Explain the difference between supervised and unsupervised machine learning.
The difference between supervised and unsupervised machine learning is the way labeled data is treated. Unsupervised learning doesn’t need labeling data, while supervised learning needs it.
17. What is a Fourier transform?
Your candidates should state that a Fourier transform is a method that decomposes functions into spatial or temporal frequency functions.
It’s a typical route to pulling out features from audio signals and other time series.
18. When assessing if a machine learning model is effective, what evaluation approaches would you take?
You’re looking for candidates who can explain they would use cross-validation techniques to segment the dataset or split it into test and training sets. Then, they’d apply a collection of performance metrics.
What’s crucial here is that your candidates show you they understand that accurately measuring models depends on choosing the right measures for the right citation.
19. Write down the pseudo-code for a parallel implementation of your choice of algorithm.
This question helps you see if your candidate can write code while thinking in parallelism.
It shows whether they could handle concurrency in programming implementations that deal with big data.
20. Is it possible to cut two strings, A and B, that are the same length at a common point so that the first section of A and the second section of B create a palindrome?
While this is a software engineering question, it’s useful to test whether your candidates are knowledgeable about data structures and algorithms. There are several routes to checking for palindromes.
21. Explain how you would implement a recommendation system for our company’s customers.
This is an opportunity for your candidates to demonstrate they’ve researched your company and industry.
A strong candidate would show they understand what drives revenue for your company and the types of customers your business has. And they would explain how they could implement machine learning models to solve your company’s problems.
22. Where would you typically source datasets from?
This is another question to test whether your candidate is truly interested in machine learning.
Someone who genuinely loves machine learning is likely to have created their own side projects and, therefore, is aware of where to get great datasets. This type of question helps you sort out passionate engineers from engineers who just work for a salary.
23. Have you trained models for fun? What hardware or graphics processing units did you use?
This question helps you find candidates who have undertaken machine learning projects in their spare time, not just in corporate jobs. It tests whether your candidates can apportion GPU time effectively and if they know how to resource projects.
24. How would you approach the ‘Netflix Prize’ competition?
Skilled candidates will be aware of the Netflix Prize, a contest where Netflix offered a prize of $1 million to anyone who could create a better collaborative filtering algorithm.
BellKor (the winners) used several different methods to create a 10% improvement in the algorithm. Strong candidates will recall not only the contest but also the solution BellKor created, which would demonstrate that they have been passionate about machine learning for a long time.
25. Explain how primary and foreign keys are linked in SQL.
Machine learning engineers must be proficient in many key data formats, including SQL. Answers to this question will show if your candidate can manipulate SQL databases.
They should explain they could match up and join tables using foreign keys and a corresponding table’s primary key. They should also walk you through how they would set up SQL tables.
26. Have you used Spark or other big data tools?
Spark is the most in-demand big data tool. However, if your company uses a different tool, feel free to mention that instead of Spark.
This question will help you identify candidates who are familiar with these tools and be able to hit the ground running. Answers will also show you who has spent time researching and familiarizing themselves with your company before the interview.
27. When do you think ensemble techniques might be practical?
Here, you’re testing your candidate’s ability to increase predictive power. Ensemble techniques combine different learning algorithms to create an enhanced predictive performance.
This approach creates a robust model typically resistant to small changes in data that could skew prediction accuracy. Experienced candidates will be able to list ensemble method examples, such as the ‘bucket of models’ method, bagging, boosting, and more.
28. Explain the difference between a discriminative and a generative model.
Your candidate should understand that a discriminative model just learns the difference between data categories while a generative model learns data categories.
They should also state that for classification tasks, a discriminative model will usually outperform a generative one.
29. How is L1 regularization different from L2?
L1 regularization is more sparse as variables are assigned either a 0 or 1 (binary). L2 regularization spreads errors among terms.
30. What is precision? What is recall?
Precision is the number of accurate positives claimed by the model in comparison to the number of positives claimed. This is also called positive predictive value.
Recall is the number of positives claimed in comparison to the number of positives found in the data. This is also known as the true positive rate.
31. Explain the trade-off between variance and bias.
Variance error happens when the learning algorithm is too complex. This could create an overly sensitive algorithm, leading your model to overfit data.
Bias error happens when the learning algorithm has over-simplified assumptions. This creates the opposite issue to variance error. Bias error could cause generalization of knowledge from training to test set and the model underfitting data. This would lead to a model that can’t have high predictive accuracy.
Your candidate should show they understand that it’s never a good idea to have a model with high variance or high bias. There needs to be a trade-off between the two.
32. What are some of your favorite APIs to explore?
This question tests if your candidate has worked with external data sources. If they have, they’re likely to have some preferred APIs. The best candidates will tell you what they think of certain APIs and give details of pipelines and experiments they’ve run.
33. Explain how XML compares to CSVs in terms of size.
This question tests whether your candidate is able to cope with data wrangling messy data formats.
XML takes up far more space than CSVs. XML uses tags to lay out a tree-like design for key-value pairs.
CSVs use separators to create categories of data and organize this data into columns. Usually, an engineer will want to process XML data into a usable CSV.
34. If you were given an imbalanced dataset, how would you handle it?
Here, you’re testing your candidate’s understanding of the damage imbalanced datasets can cause.
Your candidates should show how they would balance this damage. They can use various tactics such as resampling the dataset, collecting more data, and trying a different algorithm.
35. What do you think about the GPT-3 model?
This is another question that assesses whether your candidate follows the latest trends and news in machine learning.
Developed by OpenAI, GPT-3 is a new language generation model that can generate what appears to be human-level conversational pieces (as large as novel-size works) as well as create code from natural language.
If your candidates are passionate about machine learning, they will likely have much to say about GPT-3.
36. What are your thoughts on how Google is training data for self-driving cars?
Here, you’re testing your candidate’s understanding of different machine learning methods.
Currently, Google uses Recaptcha to find labeled data on traffic signs and storefronts.
37. How would you build a data pipeline?
This should be common knowledge for machine learning engineers. Your candidate should show familiarity with data pipeline building tools, such as Apache Airflow. They should also have in-depth knowledge of where to host models and pipelines, such as, for example, AWS, Azure, Google Cloud, and so on.
You want your candidate to talk you through their lived experience building and scaling a functioning data pipeline.
38. List some data visualization libraries you’ve used. What data visualization tools do you think are the best?
Here, you’re assessing your candidate’s ability to correctly visualize data as well as their knowledge of popular tools, such as Plot.ly, Tableau, Python’s seaborn, and more.
39. What would you do if you discovered missing or corrupted data in a dataset?
Your candidate should state that they would search for the missing or corrupted data and then replace them with another value or drop those columns or rows.
40. Define the F1 score. How would you use it?
Your candidate should state that the F1 score is a way to measure a model’s performance and that they’d use it in classification tests.
41. Explain the difference between Type I and Type II errors.
This should be a very simple question for machine learning engineers, but it’s prudent to ask the odd easy question to ensure your candidate is on top of the basics.
Type I error is a false positive. It claims something had happened when it didn’t. Type II error is a false negative. It claims nothing happened when something did.
42. How does a ROC curve work?
Your candidate should explain that the ROC curve is a graph plotting two parameters, true and false positive rates.
A key aspect to look out for here is if they understand that a ROC curve is usually used as a stand-in for the trade-off between false positives, i.e. the probability of false alarm triggers, versus true positives, i.e. how sensitive the model is.
43. Explain how your machine learning skills will help our company generate profits.
This is a great question to see if your candidate has researched your company. A good machine learning engineer understands that their skills are only good if they drive business results.
Let’s say you were hiring for Netflix. In that instance, your candidate could say that by developing a more accurate recommendation model, users would be more satisfied with the programs they watch, leading to long-term user retention and profits.
44. Give me examples of your favorite machine learning models.
This is another question to assess whether your candidate has more than just an ‘on-the-job’ interest in machine learning.
A passionate machine learning engineer will give several examples of machine learning models they like – and be knowledgeable about how each was implemented.
45. What are your thoughts on our data process?
This type of question allows you to see if your candidate can be a valuable addition to the current team.
A great candidate will show they understand why your data process has been set up in a particular way. They will give you constructive, insightful feedback.
46. In machine learning, what are the three model-building stages?
This is a simple question, but it ensures your candidate knows the basics.
The three model-building stages in machine learning are:
Model building, where the engineer chooses a suitable algorithm and trains it to criteria given to them
Model testing, where the engineer uses test data to check the model’s accuracy
Model application, where the engineer makes required amendments post-testing and starts to use the model in real-time
It’s also a good sign if your candidate mentions that, once they’ve completed the model application stage, they would need to check the model every now and then to ensure it works correctly and is up-to-date.
47. Explain the differences between machine and deep learning.
Deep learning is a type of machine learning, but this question will help you determine whether your candidate understands the key differences.
The five main differences between machine learning and deep learning are, as follows:
Machine learning is when machines make their own decision using past data. Deep learning is when machines do this using artificial neural networks.
Machine learning only needs a small amount of data in the initial training phase. Deep learning needs a large amount of data.
Machine learning doesn’t need high-end machines as they don't need a lot of computing power. In contrast, deep learning requires high-end machines.
With machine learning, an engineer must identify and manually code most features. With deep learning, the model uses the data it receives to learn features itself.
With machine learning, the machine separates the problem into two sections, individually solves them, and then combines them. With deep learning, the machine solves the problem end-to-end.
48. List some supervised machine learning applications used in modern businesses.
Again, you’re testing your candidate's ability to understand some common real-world applications of machine learning.
Some great examples they can give are:
Fraud detection, in which a model can be trained to discover suspicious patterns that could imply fraud
Spam email detection, in which engineers train a model to use past data regarding the categorization of emails as spam or not spam
Document sentiment analysis, in which machine learning specialists can train a model to mine documents to find out if the overall tone is positive, negative, or neutral
Medical diagnostics, in which models can be trained to find if a patient is suffering from a disease
49. Explain the differences between inductive and deductive machine learning.
This is another basic but important question enabling you to check if your candidate has all bases covered.
The main difference is that inductive learning watches instances to draw a conclusion. Deductive learning concludes experiences.
50. How would you choose which algorithm you will use for a classification problem?
Although there are a lot of variables as to why someone would choose one algorithm over others, this question allows you to see if your candidate follows a logical thought process when selecting the right one.
Here are some examples of different problems and possible solutions:
Problem: Training dataset is small. Solution: Use models with high bias and low variance.
Problem: Training dataset is large. Solution: Use models with low bias and high variance.
Problem: Low accuracy issue. Solution: Test and cross-validate different algorithms.
51. What are your thoughts on Amazon’s recommendation engine? How does it work?
Once a user buys something from Amazon, Amazon stores that purchase data for future reference and finds products that are most likely to be bought.
Future recommendations are made possible by the Association algorithm, which can identify patterns in a given dataset.
52. Define Kernel SVM.
SVM stands for support vector machine. These are a class of algorithms that analyze patterns.
53. Explain how you would build an email spam filter.
Your candidate should show they’re able to give clear, logical steps
To create a spam filter:
You need to feed the spam filter with thousands of emails previously categorized as “spam” or “not spam”
The supervised machine learning algorithm then starts to detect emails likely to be spam based on words used within these emails (e.g., free offer, lottery, etc.)
The spam filter then uses algorithms like support vector machines (SVM) and decision trees, as well as statistical analysis to sort new incoming emails into “spam” or “not spam”
If it determines that the likelihood of spam is high, it will label it as such, and the email will not enter the inbox
The engineer then needs to test the accuracy of the model to determine the best algorithm to use, i.e. the one with the highest spam detection accuracy
54. Explain what a recommendation system is.
In layman’s terms, a recommendation system is an information system that predicts what a user would like to see by filtering through previous user choice patterns.
Recommendation systems send you product recommendations from Amazon based on what you've previously purchased, for example. They’re also used by Netflix when the platform recommends shows you may like to watch.
55. Considering that many machine learning algorithms exist, how would you choose an algorithm for a particular dataset?
Here, you’re checking to see if your candidate can demonstrate logical reasoning and critical thinking when making choices.
There is no ‘perfect’ algorithm that works for every situation. Therefore a good engineer will choose an algorithm using these questions:
What is the company’s goal?
Is the data labeled, unlabeled, or mixed?
Does the problem relate to clustering, regression, classification, or association?
How much data is there?
Is the data categorical or continuous?