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 machine learning engineer 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.
In this article, we’ve also compiled a list of 55 machine learning engineer interview questions you can use in your interviews or include as custom questions in assessments.
We’ve also provided sample responses and explained the reasons why these answers are important.
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.
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.
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
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.
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?
The best candidates will keep abreast of the latest scientific reports on machine learning. Look for well-referenced journals, such as Nature.
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.
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.
Candidates with published research papers can really stand out here – this demonstrates valuable scientific and academic experience.
Your candidate should show they understand the six basic JSON data types: objects, strings, arrays, booleans, numbers, and null values.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
L1 regularization is more sparse as variables are assigned either a 0 or 1 (binary). L2 regularization spreads errors among terms.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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.
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.
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.
SVM stands for support vector machine. These are a class of algorithms that analyze patterns.
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
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.
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?
Machine learning is becoming more and more important every year. The application and use cases are growing: Today, it’s even used in recruiting technology. Therefore, finding the best machine learning engineers is crucial for your organization.
First, you should write clear and appealing machine learning job descriptions to attract the most qualified candidates. You should also use the best machine learning engineer interview questions, which we’ve provided in this article.
Another invaluable selection method you can use is skills testing, which is efficient, cost-effective, and helps you hire bias-free. Assess applicants’ skills at the beginning of your recruitment process to identify your best talent and invite only qualified candidates to an interview.
This approach can effectively replace CV screening, which can be very resource-intensive and biased.
For the best results, use our Machine Learning and Data Science tests to assess candidates skills in machine learning, neural networks, deep learning, and statistics.
With TestGorilla by your side, you can hire exceptional machine learning professionals at a fraction of the time you’d otherwise need – and help your organization achieve its goals.
Register for free today and start making better hiring decisions, faster and bias-free.
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