Microsoft Machine Learning Engineer Interview Questions + Guide in 2024

Microsoft Machine Learning Engineer Interview Questions + Guide in 2024Microsoft Machine Learning Engineer Interview Questions + Guide in 2024


For decades, Microsoft has been a global tech giant shaping the digital landscape with its amazing products, such as Windows and Office. Apart from Windows and Office, it tackles healthcare, education, and environmental challenges, with cloud computing and AI leaving its mark on billions of lives.

Machine learning engineers at Microsoft play an important role in developing machine learning models that have a real-world impact. You’ll be able to collaborate with brilliant minds from diverse backgrounds and learn from different perspectives. Microsoft values its employees and invests in their long-term growth and success, offering competitive salaries and exciting perks.

If you are a machine learning engineer thinking of applying to Microsoft, this guide is tailored for you, providing insights into the interview process, covering interview questions, and valuable tips.

Getting ready for an Machine Learning Engineer interview at Microsoft? The Microsoft Machine Learning Engineer interview span across 10 to 12 different question topics. In preparing for the interview:

  • Know what skills are necessary for Microsoft Machine Learning Engineer roles.
  • Gain insights into the Machine Learning Engineer interview process at Microsoft.
  • Practice real Microsoft Machine Learning Engineer interview questions.

Interview Query regularly analyzes interview experience data, and we've used that data to produce this guide, with sample interview questions and an overview of the Microsoft Machine Learning Engineer interview.

What is the Interview Process Like for a Machine Learning Engineer Role at Microsoft?

The interview process is usually three to four rounds, where they assess your experience with training pipelines and machine learning models and your ability to develop and apply machine learning and AI algorithms to solve complex problems.

1. Application

You can apply on the Microsoft Careers page or by connecting to a recruiter via LinkedIn. You can also seek a referral through an existing employee.

2. Recruiters Screening

In this round, expect a discussion with the recruiter related to your background, experiences, and your motivation to apply for the role. The recruiter will focus on assessing your fit for the position and evaluate how well your skills and experiences align with the requirements.

3. Live Coding Assessment

This assessment will likely be on a coding platform like CoderPad or Microsoft’s own online tools. The problem questions can be related to time and space complexity, decision trees, linear regression, neural networks, arrays, and matrices.

4. Technical Round

You may have one or more technical interviews. In this round, expect coding challenges, algorithmic discussions, and scenario-based questions to assess your problem-solving ability and understanding of core ML concepts.

5. Behavioral and Cultural Fit Round

This round will focus on assessing your fit within the team and the company culture. You will be asked questions about your previous work experiences, how you handle challenges, and your teamwork and communication skills.

6. Managerial Round

If you receive an offer, you’ll have the opportunity to interact with your potential future manager. Show your enthusiasm for the specific team and project by showcasing how your skills and contributions directly align with the goals of the team.

What Questions Are Asked in a Microsoft Machine Learning Engineer Interview?

The interview focuses on a multi-faceted range of areas and concepts, aiming to assess your technical expertise, problem-solving skills, and overall fit for the Machine Learning Engineer role and their AI culture. Here’s a breakdown of the key areas you can expect:

  • Core ML concepts and algorithms
  • Coding and problem-solving skills
  • Azure Machine Learning
  • Microsoft AI products and tools
  • Design and experimentation
  • Communication and Collaboration

Here are some questions commonly asked at a Machine Learning Engineer Interview:

1. How do you decide on the most suitable algorithm for a given problem?

Choosing the most suitable algorithm for a given problem is an important step in machine learning, as it directly impacts the accuracy, efficiency, and overall success of a model. This question tests your adaptability to handle diverse scenarios and your familiarity with a range of machine learning algorithms.

How to Answer

Consider a range of algorithms suitable for the problem. Take into account factors such as the size and nature of the dataset, the complexity of the task, and the interpretability of the model.


“My approach would involve a comprehensive analysis. For example, if dealing with structured data where interpretability is crucial, I might lean towards decision trees or ensemble methods. On the other hand, for complex tasks involving unstructured data, I would consider deep learning approaches like convolutional neural networks.”

2. How do your skills and experience align with the requirements of this position?

This question allows the interviewer to test your understanding of the Machine Learning Engineer position’s responsibilities and how your skills and experience specifically translate into value for the team at Microsoft.

How to Answer

The answer should emphasize on your skills and experiences directly related to the position. Provide specific examples from your past experiences. Describe your problem-solving skills and your capacity to approach complex machine-learning problems with creativity.


“I believe my skills and experiences make me a strong fit for this role at Microsoft. In my previous role, I successfully led a team in developing a recommendation system that resulted in a 20% increase in user engagement. My proficiency in Python and deep understanding of machine learning frameworks, such as TensorFlow and PyTorch, align with the technical requirements outlined in the job description. Moreover, I adapt quickly to new technologies, and I actively follow the latest advancements in machine learning.”

3. Imagine your ideal day as a Microsoft Machine Learning Engineer. What would it look like?

The ideal day often reflects the candidate’s desired work environment and values. Asking about it allows the interviewer to understand what truly excites the candidate about machine learning and Microsoft as a company.

How to Answer

Describe an ideal day at Microsoft as a Machine Learning Engineer by emphasizing your enthusiasm for challenging projects and collaboration. Connect your vision to Microsoft’s values, showcasing a commitment to innovation and continuous learning.


“In my ideal day at Microsoft as a Machine Learning Engineer, I would thrive on tackling challenging projects, collaborating with a diverse team, and leveraging cutting-edge technologies. The prospect of making a real-world impact and contributing to Microsoft’s culture of innovation excites me. Additionally, staying updated on the latest advancements in the field and continuous learning would be integral parts of my fulfilling day.”

4. Tell me about a situation where you had to change your mind about a technical decision based on new information or feedback. How did you handle it and adjust your approach?

Adapting to new information is important in the rapidly evolving field of machine learning. This question allows the interviewer to test your willingness to change your mind, and analyze new information when presented with new evidence or feedback and your ability to learn and adjust your approach.

How to Answer

Select a past project where a technical decision needed adjustments due to new feedback. Describe how you handled the need for adjustment. Discuss the steps you took to re-evaluate the situation.


“In a recent project, I initially chose a complex ensemble model based on the available training data and the problem’s complexity. However, during a collaborative review session, a team member provided valuable feedback on the potential for model interpretability issues and suggested considering a simpler architecture. Acknowledging the merit in their input, I reevaluated the model’s complexity, performed additional analyses, and realized that a simpler model could indeed provide comparable performance while being more interpretable. I promptly adjusted the technical decision, opting for a simpler model that not only addressed the interpretability concerns but also improved overall project efficiency.”

5. Which of our products would you want to work on and why?

Microsoft has various products, and by asking this question, the interviewer checks if the candidate has researched Microsoft’s products and understands what truly excites them about Microsoft and its offerings.

How to Answer

Show your understanding of Microsoft’s diverse products. Don’t be too generic and say “all of them.” Pick a specific one that genuinely excites you, and you would want to work on.


“I’d really want to work on Azure Cognitive Services, especially the Custom Vision service. I love the idea of helping developers easily create custom image recognition models, which aligns perfectly with my passion for democratizing AI and making its capabilities accessible to a wider audience. I’m eager to work with the talented team at Azure Cognitive Services to find ways to make AI simpler and more accessible for everyone.”

6. If a linear regression model has a high R-squared value but several statistically insignificant coefficients, how would you interpret this result?

The question presents a scenario that requires careful analysis and a nuanced approach. It tests the candidate’s understanding of R-squared, its limitations, and the importance of statistical significance in model interpretation.

How to Answer

Propose different hypotheses to explain the observation. Suggest approaches to validate your hypotheses and gain deeper insights. Based on your analysis, provide a nuanced interpretation of the model’s performance.


While a high R-squared value typically indicates a good model fit, facing statistically insignificant coefficients alongside would require careful analysis. I’d start by examining correlations between features using correlation matrices or VIF scores. I’d investigate potential interactions between features using model terms like X1X2 or visualization techniques like partial dependence plots. Then I’d assess overfitting using techniques like cross-validation or regularization (L1/L2). I’d explain to stakeholders that while overall model fit is good, individual feature contributions require deeper understanding. Based on findings, I’d recommend appropriate actions such as addressing multicollinearity by removing redundant features or using regularization.”*

7. What are the fundamental steps involved in the backpropagation process within a neural network?

Backpropagation involves computations with gradients and matrix operations. This question tests your understanding of mathematical concepts and algorithmic thinking.

How to Answer

Go beyond just listing the steps. Discuss potential variations of backpropagation, their advantages and disadvantages, and your experience with their implementation in real-world projects.


“The backpropagation process in a neural network involves making predictions, calculating errors, and adjusting internal parameters to minimize these errors. Variations like stochastic gradient descent (SGD) or mini-batch gradient descent offer trade-offs in efficiency and stability. In real-world projects, finding the right balance and incorporating techniques like dynamic learning rates or regularization is key for optimal performance and generalization. Experimentation and fine-tuning are crucial in adapting these variations to specific tasks and datasets.”

8. Suppose you’re using the k-nearest neighbors (kNN) classifier on a perfectly separable dataset. What would happen to the decision boundary as you increase the value of k?

Perfect separability risks overfitting, as models can perfectly learn the training data but fail to generalize to unseen data. This question tests your grasp of the core principles of the kNN algorithm, especially how the choice of k influences model behavior and decision boundaries.

How to Answer

The answer should focus on how varying k affects the decision boundary in kNN. Highlight the risk of overfitting with high k values. Discuss how the choice of k and decision boundary complexity impact model explainability.


“As you increase the value of k in a k-nearest neighbors classifier on a perfectly separable dataset, the decision boundary becomes smoother. Initially, with smaller values of k, like k=1, the decision boundary tends to be more jagged, closely following the intricacies of the training data. This can lead to overfitting, where the model is too tailored to the training set. On the other hand, as k increases, the decision boundary becomes more generalized, smoothing out local variations. However, excessively large values of k might oversimplify the model and result in underfitting.”

9. Given two arrays with each index representing a free and busy slot for two different individuals. Find the maximum contiguous slot size when both slots are free.

This basic data structures question can be asked to test your ability to analyze a problem, break it down into smaller steps, and design an efficient algorithm to solve it.

How to Answer

Describe your approach clearly. Mention that you would iterate through the arrays, checking for free slots, counting contiguous segments, and updating the maximum size.


“To find the maximum contiguous slot size when both slots are free for two different individuals, I would start by iterating through the arrays simultaneously. At each index, I would check if both individuals are free. If they are, I would start counting the contiguous free slots and keep track of the maximum size encountered so far. If a busy slot is encountered or if the end of the arrays is reached, I would reset the count. By continuing this process throughout the arrays, I can identify the longest stretch where both individuals are available simultaneously, providing the maximum contiguous slot size.”

10. How can we provide reasons for loan rejection to applicants using a binary classification model, considering that we lack access to the feature weights?

Feature weights in traditional models provide direct insights into model decisions. This question tests your ability to think creatively and propose alternative solutions to provide meaningful explanations.

How to Answer

When answering, discuss the limitations of traditional feature weights and their adaptability. Suggest alternative techniques and explain how these techniques can provide insights into the factors influencing the model’s decision.


“In the absence of direct access to feature weights in our binary classification model for loan approval, we can leverage interpretability techniques to provide reasons for rejection. One approach is to use methods like SHAP values or LIME, which offer insights into feature contributions without exposing the exact weights. For example, we could calculate SHAP values for each applicant, indicating the impact of individual features on the rejection decision. If a particular feature, such as low credit score or high debt-to-income ratio, has a high SHAP value, we can communicate this as a key reason for rejection.”

11. What steps can be taken to examine the performance of the ML model?

Model evaluation is important for ensuring the effectiveness of any machine learning project. This question tests your knowledge of relevant evaluation metrics for different types of models and tasks.

How to Answer

While answering, start by mentioning commonly used evaluation metrics. Discuss the significance of cross-validation to assess model performance. Mention more advanced techniques like ROC curves for a deeper understanding of model performance.


“Evaluating a machine learning model involves using standard metrics like accuracy, precision, recall, F1 score, and AUC-ROC. Choosing the right metric depends on the business context, prioritizing aspects like precision in fraud detection. Cross-validation helps assess generalization, and advanced tools like ROC curves provide deeper insights, ensuring the model meets reliability criteria.”

12. When building a tree-based model for classification, what strategies would you employ to address overfitting?

Overfitting is a common challenge when building ML models. This question could be asked to assess your understanding of machine learning model optimization, particularly in the context of tree-based models.

How to Answer

Start by mentioning the importance of pruning techniques and highlighting the use of regularization parameters. Describe the significance of cross-validation to fine-tune model hyperparameters.


“To address overfitting in tree-based classification models, I would implement pruning techniques, restricting the maximum depth and setting minimum samples per leaf. Additionally, regularization parameters, such as controlling the number of features in each split, can be employed. Cross-validation helps fine-tune these parameters for optimal model configuration and mitigates overfitting by assessing performance on different data subsets.”

13. How would you determine the middle element of a linked list efficiently using a single pass?

Although not directly related to ML algorithms, linked lists and similar data structures are often used in implementing model architectures and data pipelines. A strong understanding of these is important for understanding and optimizing ML code.

How to Answer

To answer this, begin by explaining the approach of traversing the linked list with two pointers. Address how the approach handles both odd and even-length lists.


“To find the middle element of a linked list efficiently in one pass, use a two-pointer approach. One pointer moves twice as fast, and when the faster one reaches the end, the slower pointer is in the middle. This approach works seamlessly for both odd and even-length lists, ensuring reliability and efficiency without extra passes.”

14. How would you build a model for bidding on a new keyword using a dataset with columns for keywords and corresponding bid prices?

Microsoft often deals with large-scale online advertising, and constructing effective bidding models is important for optimizing ad placements and maximizing returns.

How to Answer

Start by mentioning the importance of selecting relevant features from the dataset. elect a suitable machine learning model for regression or prediction. Explain the process of training the chosen model on the dataset.


“To construct a bidding model for a new keyword, I would start by selecting relevant features from the dataset, including bid prices, historical performance, and keyword relevance metrics. I might opt for a machine learning model like linear regression, decision trees, or a random forest ensemble. Training the model involves splitting the dataset into training and testing sets, and I would use metrics like Mean Absolute Error or Root Mean Squared Error to evaluate its accuracy. To prevent overfitting, I’d employ regularization techniques, ensuring the model generalizes effectively to bid on new keywords.”

15. How can you obtain the reversed InOrder traversal of a tree?

Strong algorithmic and data structure skills are important for developing and optimizing machine learning models. While it may not have a direct application in machine learning, the interviewer may ask to test your skills in data structures.

How to Answer

Clearly explain the process of performing an InOrder traversal of the tree while reversing the order of traversal. If relevant, discuss scenarios in machine learning where understanding tree structures might be beneficial.


“To obtain the InOrder reversal of a tree, we can perform an InOrder traversal while reversing the order of visiting nodes. This involves first visiting the right subtree, then the root, and finally the left subtree. While this specific task may not be directly tied to machine learning, a strong grasp of tree traversal techniques is crucial for various algorithms and optimizations. For instance, understanding tree structures becomes valuable when dealing with decision trees, a common component in machine learning models.”

16. What could lead to fluctuations in success rates when applying the same machine learning algorithm to the same datasets?

When dealing with large datasets, fluctuations in success rates may indicate issues in model robustness or dataset variability. This question tests your understanding of the factors influencing the stability and reliability of machine learning models.

How to Answer

Discuss that fluctuations in success rates can stem from inherent variability in the dataset. Mention how the sensitivity of the machine learning algorithm to small changes in the input data can lead to fluctuations.


“Fluctuations in success rates when applying the same machine learning algorithm to the same datasets can be attributed to several factors. Dataset variability, including changes in data distribution or the presence of outliers, may impact model performance. Sensitivity of the algorithm to small changes in input data, feature engineering choices, and the influence of model hyperparameters are also key contributors.”

17. Describe the virtual machine in Azure and outline the process of creating one in the Azure environment.

Microsoft’s Azure cloud platform is widely used for deploying and managing machine learning models, and virtual machines (VMs) are fundamental components for such tasks. Hence, this question assesses your familiarity with Azure VMs and tests your ability to work within the Microsoft ecosystem.

How to Answer

Start by providing a concise definition of an Azure Virtual Machine. Detail the steps involved in creating an Azure Virtual Machine. If applicable, highlight the relevance of Azure VMs in the context of machine learning projects.


“An Azure Virtual Machine is a scalable and flexible computing resource in the Azure cloud, allowing users to run virtualized Windows or Linux servers. To create an Azure Virtual Machine, you can access the Azure Portal, navigate to the Virtual Machines section, and specify configuration details such as the operating system, size, and networking. This process ensures a seamless deployment of the virtual machine.”

18. How would you handle the bias-variance tradeoff when building machine learning models for loan approvals, considering diverse features, to ensure the right balance between simplicity and flexibility?

Balancing accuracy and flexibility are important when building loan approval models. The interviewer wants to test your understanding of the bias-variance tradeoff and its overall impact on the performance of the model.

How to Answer

In your answer, mention that finding the right balance is crucial for developing models that generalize well to new data. Describe techniques to reduce bias and strategies to control variance.


“In building machine learning models for loan approvals, handling the bias-variance tradeoff is vital. To address bias, we consider more complex models or additional features, while regularization and feature engineering help control variance. Employing cross-validation ensures a balanced model that generalizes well to new data, critical for accuracy and robustness in loan approval scenarios.”

19. What are some primary Azure Machine Learning use cases?

Microsoft’s Azure Machine Learning platform offers various capabilities, this question could be asked to assess your familiarity with its practical applications.

How to Answer

Describe all the diverse use cases where Azure Machine Learning is commonly applied. Discuss how Azure Machine Learning is relevant across various industries and mention its specific features.


“Azure Machine Learning has extensive use across diverse scenarios. It is commonly employed for predictive analytics, image recognition, natural language processing, and recommendation systems. In industries like finance, healthcare, retail, and manufacturing, Azure Machine Learning proves its versatility by addressing specific challenges and enhancing decision-making processes. The platform’s features, including automated machine learning, seamless model deployment, and integration with other Azure services, contribute to its widespread adoption in real-world applications.”

20. How would you assess if a new delivery time estimate model for food orders outperforms the old model?

This question tests your ability to select appropriate metrics, design experiments, and interpret statistical significance, skills crucial for developing and enhancing machine learning models at Microsoft.

How to Answer

The answer should mention the key evaluation metrics suitable for the task. Then explain your approach to designing an experiment to compare the two models. Discuss the importance of statistical significance.


“To evaluate if a new delivery time estimate model for food orders is superior to the old model, I would compare their performance using relevant metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), or other accuracy measures. Additionally, conducting statistical tests, like a t-test or a paired-difference test, can help determine if the differences in performance are statistically significant.”

Tips When Preparing For a Machine Learning Engineer Interview at Microsoft

Landing a Machine Learning Engineer role at Microsoft is no easy feat, but with the right preparation and mindset, you can ace the interview process. Here are some tips to help you ace your interview with confidence:

1. Revise Fundamentals

Brush up on core ML concepts like linear algebra, calculus, probability, statistics, optimization algorithms, and machine learning models (regression, classification, decision trees, neural networks, etc.).

You can try our Learning path for Machine Learning to revise fundamentals and prepare for your interview.

2. Practice Problem-Solving

Refresh your algorithmic thinking skills by solving coding challenges and questions related to data structures, algorithms, and machine learning.

Don’t forget to practice machine learning and programming questions at Interview Questions, where we have provided different practice questions from top tech companies.

3. Practice Mock Interviews

Practice interview scenarios with friends, colleagues, or online platforms to get comfortable with the format and receive feedback. This will enhance your practice, and you will be more confident when giving the actual interview.

At Interview Query, you can check our Mock Interviews to practice and enhance your interview skills.

4. Participate in Kaggle competitions

Enhance your practical skills and problem-solving abilities through real-world applications of machine learning by participating in online competitions such as on Kaggle.

You can also check our Challenges feature on Interview Query, where you can participate in challenges, test your mettle against others, and see how you rank with competitors.

5. Be Confident and Showcase Communication Skills

Microsoft values effective communication. Practice explaining complex machine learning concepts in a clear and concise manner. Be ready to discuss your previous projects and articulate your contributions. Be confident during the interview.

Our Interview Experience feature offers a unique opportunity to explore recent interviews at top tech companies. Gain valuable insights, learn from others’ experiences, and build confidence.


Below are some of the FAQs asked by people who are interested in working as a machine learning engineer at Microsoft.

What is the average salary for a Machine Learning Engineer at Microsoft?


Average Base Salary


Average Total Compensation

Min: $88K
Max: $217K
Base Salary
Median: $145K
Mean (Average): $147K
Data points: 74
Min: $6K
Max: $351K
Total Compensation
Median: $180K
Mean (Average): $181K
Data points: 71

View the full Machine Learning Engineer at Microsoft salary guide

The average base salary for a Machine Learning Engineer at Microsoft is $147,184. Adjusting the average for more recent salary data points, the average recency-weighted base salary is $147,340.

You can check out more about average base salaries and average salaries for machine learning engineers at the Machine Learning Engineer salary page.

Where can I apply as a Machine Learning Engineer apart from Microsoft?

The job market for machine learning engineers is booming, and there are many opportunities.

Apart from Microsoft, you can apply to Netflix, Airbnb, Ipsy, DataRobot, and many other companies. Look for companies that align with your personal values and work-life balance goals.

Are there job postings for Machine Learning Engineer roles at Microsoft?

Yes, we regularly update our Job Board with current openings at different tech companies. Currently, there are postings for Machine Learning Engineers at Microsoft.

But to apply for the role, you’ll have to visit Microsoft’s Career page.


If you want more insights about the company, check out our main Microsoft Interview Guide, where we have covered many interview questions that could be asked. We’ve also created interview guides for other roles, such as software engineer and data analyst, where you can learn more about Microsoft’s interview process for different positions.

At Interview Query, we empower you to unlock your interview prowess with a comprehensive toolkit, equipping you with the knowledge, confidence, and strategic guidance to conquer every question and challenge.

You can check out all our company interview guides for better preparation, and if you have any questions, don’t hesitate to reach out to us.

Good luck with your interview!