Microsoft Machine Learning Engineer Interview Guide

Interview Guide

May 11

The role of a Microsoft Machine Learning Engineer

A MLE at Microsoft is a pretty technical role that involves developing and implementing complex machine learning algorithms and systems to solve challenging business problems. MLEs at Microsoft work with large datasets and develop sophisticated models that can analyze data and make predictions based on it.

In addition, a MLE at Microsoft will also be responsible for implementing scalable and efficient machine learning infrastructure, such as distributed computing and cloud-based technologies. They will also need to stay up to date with the latest machine learning techniques and technologies, and evaluate their potential applications within the company.

At Microsoft, there are MLE positions available across different teams and departments. These positions include the general MLE position where engineers design and implement machine learning solutions for various teams, Applied MLEs who develop solutions for specific business units, Speech and Language MLEs who work on NLP and speech recognition solutions for Cortana, Teams, and Skype, Computer Vision MLEs who develop solutions for products like HoloLens, Kinect, and Windows Hello, and Autonomous Systems MLEs who create machine learning solutions for autonomous systems such as drones and self-driving cars.

The teams and departments that MLEs work in vary depending on the specific position. MLEs may work in research teams, product teams, or customer-facing teams. For example, an MLE working on speech recognition solutions may work in the Cortana or Teams product teams, while an MLE working on autonomous systems may work in the Microsoft Garage or Microsoft Research. Ultimately, the MLE's position and team will depend on their skills, experience, and interests, as well as the specific needs of the department or team they are working with.

How to Apply for a Machine Learning Engineer Job at Microsoft?

Check out Microsoft’s career page and browse through MLE job listings. When you find a role that interests you, be sure to read through the job requirements and qualifications carefully to ensure you meet the criteria. If you have any connections within the company, consider reaching out to them for a referral as it highly increases your chance. When you apply, make sure to tailor your resume to align with the qualifications listed in the job posting. This will help you stand out from other applicants. And if you need help with customizing your resume specifically for Microsoft (or for that matter, any other company), Prepfully provides resume review services by experienced recruiters in your target company that can give you feedback on your resume. It's worth noting that the application process may vary depending on the position and location, and the company may conduct additional assessments or interviews as part of the selection process.

Microsoft Machine Learning Engineer Interview Guide

As a part of the Microsoft Machine Learning Engineer interview, you will need to go through multiple interview rounds. The interview process and questions may differ for different positions and roles.

  1. Recruiter Screen - The first primary round will consist of a recruiter screen with ML background related questions.
  2. Onsite Interview Rounds - In this round, you will face multiple interview rounds. Mainly, you can expect to face these rounds: DSA round, ML round and Behavioral round.

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Microsoft MLE: Recruiter Screen


The first round will be a recruiter screen. This screen will be designed to help the recruitment team learn more about your background, experience, and qualifications related to Machine Learning. During the call, you can expect to discuss your previous work experience, technical skills, and your interest in the company. You will be asked a range of questions related to Machine Learning and its various applications.

Interview Questions

  • Why do you want to join Microsoft?
  • Why do you think you will be a good fit for the role?
  • How many years of experience do you have in machine learning?
  • What are you passionate about?
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Microsoft MLE: Onsite Interview Rounds


In this round, you will face multiple interview rounds. Mainly, you can expect to face these rounds:

1. DSA Round: In this round, you will be tested on your problem-solving skills and your ability to write efficient code. You can expect to face questions related to data structures such as arrays, trees, graphs, and algorithms such as searching, sorting, and dynamic programming.

2. ML Round: In this round, you will be tested on your understanding of machine learning concepts and your ability to work with different supervised learning models. You can expect questions related to model performance, feature selection, regularization techniques, and different types of supervised learning models such as logistic regression, decision trees, and random forests. You will also be asked basic questions related to neural networks, such as activation functions and backpropagation. Finally, you will be asked questions related to linear regression, such as the assumptions, model fitting, and regularization techniques.

3. Behavioral Round: In this round, you will be evaluated on your background specific knowledge and your ability to work on projects. The questions may be tailored to your specific background. For example, one candidate's background was NLP so he was asked questions related to NLP diving deep on topics like BERT and its architecture. You can expect to discuss your experience working on different projects and how you dealt with any challenges that arose.

Interview Questions

  • Decide if two given tokens are the same.
  • Differences between L1 and L2 regression.
  • Draw out BERT's architecture and explain it.
  • What is the definition and application of stack and heap?
  • What is the time complexity of a linear search algorithm?
  • How would you select the value of "k" in the K-means algorithm?
  • What is a perceptron?
  • Explain the basic steps of backpropagation in a neural network?
  • How do you prioritize tasks when working on a project with competing deadlines?

Tips to ace the Microsoft MLE Interview

When you are preparing for a Microsoft Machine Learning Engineer Interview - we’d recommend keeping these things in mind:

  • Research the company. Before going for the interview, research the company's history, mission, products, and culture. Understand the position and responsibilities you are applying for. Check out Microsoft’s values page to prepare better for the interview.
  • Prepare for the technical rounds. Brush up on your technical skills, especially in the areas of data structures, algorithms, machine learning, and neural networks. Practice solving problems related to these topics.
  • Emphasize your experience Be prepared to discuss your previous work experience related to machine learning, including projects you have worked on, technologies you have used, and your contributions to the team.
  • Be specific when answering questions and provide examples from your previous work experience. Give details on how you approached a problem, the tools and techniques you used, and the results you achieved.
  • Demonstrate your passion for machine learning and how you stay up-to-date with the latest developments in the field. Be ready to discuss your favorite machine learning projects and technologies.
  • Practice answering interview questions with a friend or colleague, and ask for feedback on your responses. You can also check out our mock interviews, where MLEs from Microsoft will help you prepare for your interview.

Responsibilities of a Machine Learning Engineer at Microsoft

The responsibilities of a Machine Learning Engineer at Microsoft across roles can broadly be seen as-

  • Assessing the viability of integrating existing Machine Learning models. For instance, a Machine Learning Engineer at Microsoft may assess the viability of integrating an existing sentiment analysis model developed by a research team into the Teams product to enable users to analyze the sentiment of chat conversations.
  • Keeping up to speed with the current academic and industry advances in Machine Learning techniques and experimenting with their application. For example, applying these techniques to improve the accuracy of the object recognition algorithm in Microsoft HoloLens.
  • Building strong working relationships across multiple internal disciplines and teams
  • Continually seek deeper insights into the performance and scalability of Microsoft’s system
  • Collaborating closely with product, design, and engineering to improve the user experience of Microsoft's products, such as the handwriting recognition feature in OneNote.
  • Optimizing performance and tracking down and fixing customer-facing issues. For instance, optimizing the performance of the machine learning models powering the personalized search feature in Bing and track down and fix any customer-facing issues, such as incorrect search results.

Skills and Qualifications needed for Machine Learning Engineers at Microsoft

Here are some skills and qualifications that will help you excel in your Machine Learning Engineering interviews at Microsoft. One thing to note here is that the degree qualification is different for every role.

  • It's beneficial to have at least 5+ years of experience in DS, SE and MLE roles, which can help you stand out from other candidates.
  • Gain experience with machine learning tools such as ONNX, PyTorch, TensorFlow, and scikit-learn. This can demonstrate your ability to build models and analyze data, which are critical skills for an MLE role.
  • Build data pipelines using cloud computing.
  • Demonstrate experience working across the data stack of a variety of complex projects while providing technical and thought leadership. This can show that you have a broad understanding of the data science and engineering process and can contribute to the success of complex projects.
  • Be proficient in big data platforms and tools such as Hadoop, Hive, Spark, and Kafka. This can demonstrate that you have experience working with large datasets and can use these tools to perform data analysis and modeling.
  • Experience with NoSQL, SQL Server, BI/Analytics, Kusto, Azure Synapse Analytics, Data Explorer, Cosmos, App Insights, Snowflake, and Kusto. This experience can demonstrate that you have a broad understanding of data storage, analysis, and visualization tools and can work with different data platforms and tools.

It's important to keep in mind that this list is not exhaustive, and the requirements and qualifications may vary depending on the position and location. It's always best to check the job description and requirements on the Microsoft's Career page before you apply for the role.

Salary Ranges

On average, Machine Learning Engineers at Microsoft can expect a base salary ranging from around $110,000 to $160,000 per year. However, it's important to note that this is a rough estimate and individual salaries can vary significantly based on factors such as experience, skills, and location.


The interview process for a Machine Learning Engineer role at Microsoft typically includes 2 primary rounds - a recruiter screen, and the onsite interview rounds. The first primary round will consist of a recruiter screen with ML background related questions. In this round, you will face multiple interview rounds. Mainly, you can expect to face these rounds: DSA round, ML round and Behavioral round.

Good luck with your interviews!