Google Machine Learning Engineer Interview Questions + Guide in 2024

Google Machine Learning Engineer Interview Questions + Guide in 2024


Exciting times are ahead for Google’s R&D, as the company recently revealed its newest artificial intelligence chips and announced an ARM-based central processor.

As a machine learning engineer, you will work on projects critical to Google’s needs and have the opportunity to switch teams and projects as your skills evolve. Google values versatile engineers who display leadership qualities and are enthusiastic about new problems.

If you’re looking for guidance to ace your upcoming ML engineer interview or plan to apply, you’ve come to the right place. We will cover the details of the Google interview process, along with our handpicked interview questions and strategies for answering them.

What Is the Interview Process Like for a Machine Learning Role at Google?

The Google machine learning interview is a highly structured process with a rubric of skill sets on which you’ll be graded. This role requires deep knowledge of machine learning algorithms. You can expect nuanced discussions around algorithms and their applications, so we recommend you take a few weeks to review supervised, unsupervised, semi-supervised, and reinforcement learning algorithms. In addition to theoretical concepts, the interviewers will test your business sense, i.e., how well you apply concepts in real-world optimization scenarios. Finally, cultural fit is critical, so practice common behavioral questions, particularly ones that test your “Googliness.”

Please note that the questions and structure of the interview process will differ based on the type of machine learning role; ML scientists and engineers have a similar domain but different functions. Always read the job description carefully while preparing your interview plan.

On a related note, check out our guide on preparing a solid interview strategy, or read about this ML engineer’s inspiring journey here if you’re feeling stuck.

The process generally has multiple rounds spanning a couple of months.

Step 1: Resume Screening

This is arguably the most competitive stage, so ensure your resume is tailored to show your skills in the best light. Here are some tips to ensure your resume is in top shape:

  • Tailor your CV to the job description as much as you can. Mirror their language to show you understand what is expected of you in the role.
  • Quantify your project’s impact. Mention your contribution to the project, talk about the positive outcome, and show initiative and leadership.
  • Use action verbs like “initiated,” “launched,” and “led.”
  • Lead with your strongest points. If you have less work experience, you should lead with your education, followed by relevant projects.

Step 2: Recruiter Screening

A phone interview with a recruiter will be scheduled to get a sense of your work experience and skillsets. They may also ask you why you want to join Google or ask CV-based questions, so prepare some responses to help you sail through this important step.

Tip: Ask your recruiter for pointers on the next steps and if they have resources to guide you.

Step 3: Technical Phone Screen

Next, you’ll have an hour-long technical round in which you’ll be given a Leetcode-style coding question (usually case study-based) followed by some theoretical ML questions. Even though this round is more high-level than the later ones, give detailed answers and don’t miss any edge cases. Remember that you are being assessed on your technical know-how and attention to detail.

Tip: Prepare for this round by reviewing ML concepts from a textbook or relevant blog articles. Also, read up on different kinds of optimizers, as this is a favored topic.

Step 4: Onsite Interviews

If you do well in the online technical round, you will be invited for an onsite visit. You will spend a day at one of their offices, participating in four or five rounds and speaking with managers, peers, and HR personnel. Here are the broad categories:

  • Coding round: You’ll be asked to solve data structures and algorithm problems
  • System design interview: This round is usually for Level 5 and above candidates. You’ll be asked to create a high-level design for a search algorithm or recommender system.
  • Machine learning case interview: You’ll be given a business problem and asked to solve it using a machine learning solution.
  • Behavioral/HR interview: This round focuses on your communication, leadership, emotional intelligence, and overall scrappiness to assess your cultural fit at Google.

What Questions Are Asked in a Google Machine Learning Interview?

In this section, we’ll review the various interview questions that might be asked during a Google machine-learning interview.

You can expect a mix of technical and behavioral questions. Technical questions often cover fundamental machine learning concepts, like explaining different ML models, understanding the bias-variance trade-off, and addressing overfitting. They also include systems design questions (for L5 and above), such as designing a content ranking system or scaling a recommender system. For the behavioral questions, the interviewers evaluate your alignment with Google’s values, past projects and conflict resolution, and motivation for joining Google.

Check out this video to learn more about the types of questions typically asked and why.

1. How would you prioritize projects of varying complexity?

This evaluates your strategic planning skills in aligning projects with Google’s overarching goals and timelines.

How to Answer

Highlight the importance of aligning projects with the team’s objectives and assessing resource availability and project impact. It’s important to talk about collaborating and getting member feedback on projects, as it demonstrates that you’re a team player.

Tip: For behavioral questions, describe the context, the challenge, your action, and the outcome following the STAR framework.


“I would map out each project’s potential impact on the team’s key objectives. I’d assess the resources each project requires versus what’s available, including SME support, accesses, and tools. I’d also seek input from my team to ensure all perspectives are considered. Projects should then be ranked based on their strategic importance, return on investment, and deadlines.”

2. Why do you want to join Google?

Understanding why you want to join will help your interviewer determine if your values and aspirations align with Google’s mission.

How to Answer

Demonstrate your understanding of Google’s work, culture, and the specific opportunities that attract you to the company. Be honest and specific about how Google’s offerings align with your career goals.


“Google’s commitment to innovation inspires me, particularly in AI and machine learning with projects like Gemini. Google’s approach to solving complex problems for its users aligns with my desire to contribute to meaningful projects that have a global impact. I see a unique opportunity to use my analytical skills to help enhance product features and make a tangible difference in how people access and use information.”

3. Tell me about your machine learning background.

This question gauges the depth and breadth of your expertise. Knowing more about your professional accomplishments helps the interviewers decide which team would be a good fit.

How to Answer

Tailor your response to reflect the work you’re passionate about and have explored the most. Pick the top three or four achievements that best represent your journey. Google values strong foundational concepts, so highlight any relevant education you have in the field.

Tip: For every point you include, ask yourself why it might be relevant in the interview. Link your answers to the role you’ll be expected to fill.


“I hold a master’s degree in computer science, where I specialized in machine learning and data analysis. My areas of concentration were in neural networks, IoT, and web development. During my studies, I was particularly fascinated by the application of deep learning in natural language processing, which led me to work on several projects involving sentiment analysis and chatbot development. After graduating, I joined a startup focused on AI-driven marketing solutions, where I used machine learning models to personalize customer experiences. This work involved a lot of experimentation with different algorithms and scaling solutions to handle large datasets.”

4. What is your approach to resolving conflict with co-workers or external stakeholders?

The interviewer needs to know how you handle conflicts on a team, as engineers collaborate closely with other teams in Google.

How to Answer

Illustrate with a concise example and use it to highlight your initiative and emotional intelligence.


“In a past project, I worked with a team member who tended to make unilateral decisions and had difficulty effectively communicating their thought process.

Realizing this affected our productivity and team dynamics, I requested a private meeting with this colleague. I tried to understand their perspective while constructively expressing the team’s concerns. During our conversation, I learned that their approach came from a deep sense of responsibility and a fear of project failure. I acknowledged their commitment and then elaborated on how collaborative decision-making could enhance project outcomes.

We agreed on a more collaborative approach, with regular briefings that clearly outlined updates. This experience taught me the value of addressing interpersonal challenges head-on but with empathy. The situation improved significantly after our discussion.”

5. Explain when you took the lead in a challenging situation.

Google highly values leadership qualities—even in non-leadership roles—because employees are expected to take the initiative, especially in high-stakes situations.

How to Answer

Highlight how you motivated the team and any critical decisions you made. Google loves to see some “scrappiness”—that you have an entrepreneurial mindset, can work with scarce resources, and are willing to go outside your comfort zone.


“In my previous role, when our team was facing a critical deadline for launching a new feature, the project lead unexpectedly had to take leave. I decided to coordinate the project’s final stages. I began by reassessing our priorities and redistributing tasks based on team members’ workloads. To address morale and ensure everyone felt supported, I initiated daily check-ins as a space for the team to voice concerns and progress updates. We successfully met the deadline, and the final solution received positive feedback for its functionality and user interface.”

6. How would you interpret coefficients of logistic regression for categorical and Boolean variables?

Whether it’s optimizing ad placements, improving search, or personalizing user experiences, substantiating the effect of various factors on binary outcomes is crucial.

How to Answer

Discuss the interpretation of logistic regression coefficients in the context of a typical Google business problem. Emphasize understanding the relationship between these variables and the predicted variable.


To interpret the coefficient of a categorical variable, you can consider its exponentiated value, which gives us the odds ratio. An odds ratio greater than 1 indicates that the presence of that category increases the odds of the binary outcome. An odds ratio of less than 1 indicates that the presence of that category decreases the odds of the binary outcome relative to the reference category. The magnitude of the odds ratio represents the strength of the association between the categorical variable and the binary outcome.

For Boolean variables, like whether an email is marked as important, the coefficient tells us the change in log odds of the outcome (e.g., the email being opened) when the variable switches from 0 (not important) to 1 (important). By exponentiating this coefficient, we obtain an odds ratio, which tells us how the odds of the email being opened are multiplied when it’s marked as important while controlling for other factors.”

7. Write a Python function to efficiently search for a specific record in a massive dataset. Explain the data structures and algorithms you would use to optimize search performance.

The ability to select the right data structures and algorithms could significantly impact the efficiency of Google’s services.

How to Answer

Discuss the algorithms used for optimizing search performance in massive datasets. In this case, mention the binary search algorithm and its time complexity. Explain how it works and why it is a suitable choice for sorted datasets.


“To efficiently search for a record, I’d first consider the data’s nature and access patterns. The binary search algorithm is particularly effective for sorted datasets as it offers a time complexity of O(log n), where n is the dataset’s size. The binary search algorithm works by initializing two pointers, ‘left’ and ‘right,’ at the beginning and end of the dataset, respectively. It repeatedly calculates the middle index, ‘mid,’ and compares the value at ‘mid’ with the target record. Based on this comparison, it adjusts the search range by updating ‘left’ and ‘right.’ It is an optimal choice for large datasets because it can minimize the number of comparisons required.”

8. Let’s say we are trying to improve our search feature. How would you improve recall without changing the underlying algorithm?

Dynamically improving search is a crucial aspect of Google’s business. This question checks your knowledge of their platform and ability to think critically.

How to Answer

Focus on methods that enhance data quality or modify the search process’s parameters to increase recall, emphasizing your understanding of search mechanisms.


“Recall is the ratio between the number of correct predictions and the number of predictions that were denoted as right. To improve recall in Google’s search feature, it would be necessary to enhance prediction by either changing the acceptance threshold or increasing the number of parameters to be evaluated. I would enrich the metadata and product descriptions to ensure broader coverage of relevant keywords. Additionally, adjusting the threshold for matching query terms can increase the number of returned results.”

9. Explain the trade-offs between bias and variance in machine learning models.

In machine learning projects, you need to understand this crucial trade-off to reconcile your technical knowledge with your domain expertise, for example, in a customer churn prediction problem.

How to Answer

Define bias and variance in the context of machine learning. Explain the trade-offs, emphasizing the impact of underfitting (high bias) and overfitting (high variance) on model performance. Discuss your recommended strategies to find the optimal balance.


“Bias represents the error introduced by overly simplistic assumptions in a model. When a model exhibits high bias, it tends to oversimplify the problem and underfit the data. On the other hand, variance represents the error due to excessive model complexity, causing the model to fit the training data too closely. Techniques like cross-validation to assess model performance, regularization to control complexity, and thoughtful algorithm selection will help us assess the required trade-off in a specific scenario.”

10. Let’s say you’re working on a job recommendation engine. You have access to all user LinkedIn profiles, a list of jobs each user applied to, and answers to questions the user filled in about their job search. How would you build a job recommendation feed?

This is a systems design question on a topic that is at the heart of Google’s focus on personalized customer experience.

How to Answer

The expectations for ML design or systems design questions are often unclear, and this type of problem can be large in scope. So, clarify expectations at the outset. Ask your interviewer relevant questions about the expectations around model deployment, modeling choices, and business objectives. At the beginning, mention your plan and the points you wish to touch upon.


“I would start by preprocessing the dataset to integrate LinkedIn profiles, job application histories, and survey responses. I’d then extract features that include both user-specific information (like skills and past job titles from LinkedIn profiles) and job-specific attributes (like required qualifications and job descriptions). I would use a hybrid approach for designing the algorithm by combining content-based filtering and collaborative filtering. Content-based filtering could match users to jobs based on similarity in job descriptions and user skills, while collaborative filtering would leverage patterns from user job applications to recommend jobs that similar users have applied to. This system would be implemented on a scalable platform like Google Cloud to handle real-time updates and large data volumes. I would continuously evaluate the model’s performance using metrics like precision and recall.”

11. What is the vanishing gradient problem?

The vanishing gradient problem is a critical issue when training deep neural networks, which are central to Google’s products, particularly speech recognition, image processing, and language translation.

How to Answer

Explain the problem and its implications for the training process. You’d also do well to quickly outline some common solutions.


“The vanishing gradient problem occurs while training deep neural networks, where the gradient shrinks exponentially as they are propagated back through the network from the output layer towards the input layer. This is especially problematic with activation functions like sigmoid or tanh, where the gradient can become very small. As a result, the weights in the earlier layers of the network hardly change, and the network fails to learn from the data. To combat this, we can use ReLU (Rectified Linear Unit) activation functions, which help maintain larger gradients by providing a constant gradient for positive inputs.”

12. You’re given a dataframe of standardized test scores from high schoolers from grades 9 to 12 called df_grades. Write a function in pandas called bucket_test_scores to return the cumulative percentage of students that received scores within the buckets of <50, <75, <90, <100.

Google engineers often need to categorize data to make informed decisions, and this question tests these skills in Python. Note that you have the option (before the interview) to specify whether you’re comfortable in a different language. The emphasis (at Google) is to test the efficacy of your solution and not necessarily the language you choose to code in.

How to Answer

Walk your interviewer through your code and clearly explain the functions you’d use.


“I would start by defining the score buckets as intervals that capture the ranges of interest: below 50, 50 to less than 75, 75 to less than 90, and 90 to 100. Using the pd.cut() function, I would categorize each student’s score into these buckets. Then, I would count the number of students in each bucket and calculate the cumulative percentage of students for each category. This approach ensures that we understand not just the distribution but also how large a portion of the student population falls into each performance category.”

13. If two features are highly correlated in a random forest, how will both those features appear in a measurement of feature importance?

Understanding the impact of feature correlation is crucial to gauging your depth in model interpretation.

How to Answer

Discuss how feature importance in random forests might be affected by correlated features and the potential misleading interpretations that might arise.


“Feature importance is measured using a technique called permutation importance. Permutation importance is a way of measuring the contribution of each feature to the model’s prediction accuracy by randomly permuting the values of that feature and measuring the resulting decrease in accuracy. If two features are highly correlated, they may both have high permutation importance scores because they both contribute to the model’s ability to make accurate predictions. However, the specific way in which they are combined in the model can affect how they appear in a measurement of feature importance.”

14. What is the concept of LDA, or linear discriminant analysis, in machine learning? What are some practical use cases for LDA?

LDA is a classical method for both dimensionality reduction and classification, which are important in image and speech recognition or fine-tuning search algorithms.

How to Answer

Along with describing LDA and its applications, mention the caveats, such as how LDA assumes data is normally distributed and that the classes have identical covariance matrices. Remember that your nuanced thinking is being tested through these questions.


“LDA is primarily used to project features in a dataset onto a lower-dimensional space to improve classification performance and reduce computational costs. LDA differs from other dimensionality reduction techniques like PCA because it focuses on maximizing separability among known categories. It’s only effective when the assumptions of normal distribution and equal covariance are met. Practical use cases of LDA could include improving the accuracy of facial recognition in Google Photos by reducing dimensionality while preserving the most important aspects for distinguishing faces.”

15. How would you justify the complexity of building a neural network model and explain the predictions to non-technical stakeholders?

Google values engineers who can articulate the benefits and risks of AI technologies in a way that is accessible to all stakeholders. Communicating well also proves that you have a good grasp of fundamentals.

How to Answer

Suggest focusing on the outcomes and benefits of using neural network models rather than the intricate technical details. It is also essential to ask them questions to understand their level of technical knowledge first so that you don’t come off as condescending. Your answer should reflect empathy and emotional intelligence.


“I’d first engage with them to understand their level of familiarity with AI and machine learning. Instead of technical jargon, I’d use visual aids like a simple flowchart to show how different inputs into the model affect the outputs. To justify the complexity, I’d emphasize the benefits that fit the overarching goals of the project or team. I’d also use examples from past projects to drive home the point and ensure they are equal participants in the conversation so that it doesn’t feel like I’m talking down to them.”

16. When designing neural networks for image classification, how does the Adam optimization algorithm differ in the way it works from other optimization methods?

This interview question tests your knowledge of optimization algorithms, which are key in enhancing the performance of neural network-based classification problems. Optimizers are an important concept that comes up a lot in Google’s machine learning engineer interviews. Here is a great article that explains different types of optimizers.

How to Answer

Explain Adam’s unique features compared to other optimizers and why it can be more effective for certain tasks.


“Adam optimization differs, as it combines the benefits of two other extensions of stochastic gradient descent—AdaGrad and RMSProp. It computes adaptive learning rates for each parameter. In image classification, Adam’s benefits include handling sparse gradients and non-stationary objectives effectively, making it suitable for large datasets with complex architectures. Its ability to quickly converge and its efficiency in memory usage are significant advantages over traditional optimization methods.”

17. How would you design Google Home?

Systems design questions test your technical acumen and understanding of market needs and user interface design. You can expect these questions for L5 and above in Google and other MAANG interviews.

How to Answer

As stated earlier, ask clarifying questions to determine if there are particular areas that you need to focus on. Prepare a script for answering systems design questions. If you’re looking for inspiration, have a look at this mock interview.


“Initially, I’d assume the device needs to function effectively across diverse acoustic environments and user demographics. The primary problem to solve would be developing a device that can understand and process natural language commands accurately under different conditions. Success metrics would include high accuracy of voice recognition, user retention rates, and positive user feedback on usability and integration with other devices.

For model training, the focus would be on collecting a diverse set of voice data to train deep-learning models. This would involve using advanced neural network architectures that are robust against background noise and variations in speech.

Deployment would start with a beta release to gather user feedback, which would inform further refinements. It would also be crucial to ensure the device integrates smoothly with the broader Google ecosystem, like syncing with Google Calendar or controlling a Nest thermostat.”

18. Let’s say you are developing a spam classifier to classify emails. You try several different classifiers, such as SVM, Random Forests, etc., but none of them produce satisfactory results. So, you decide to combine them using stacking. What classifier should you use as the meta-classifier in your stacking model?

Google will require you to understand these concepts for common use cases such as fraud detection or customer review analysis.

How to Answer

Discuss the rationale behind selecting a meta-classifier, considering its role in refining the predictions of the base classifiers.


“In the stacking model for a spam classifier, a good choice for a meta-classifier could be a logistic regression model. It’s effective in combining the predictions as it can interpret the outputs of the base classifiers (like SVM and Random Forests) as features and produce a final classification. Logistic regression is simple yet powerful for binary classification tasks like spam detection, making it a suitable meta-classifier to refine and improve upon the individual predictions of the base models. In complex scenarios or with more intricate data, we might consider alternative options like neural networks or decision trees, which can capture more complex relationships between the base classifiers’ outputs.”

19. Given a dictionary with weights, write a function that returns a key at random with a probability proportional to the weights.

ML engineers often need to select actions based on their expected rewards in reinforcement learning projects. This question simultaneously tests your Python and probability concepts.

How to Answer

Discuss the key components of the solution, including extracting keys and weights from the dictionary and using random.choices() for random selection.


“To achieve this, we need to extract the keys and weights from the input weighted_dict. Then, I’d implement the random.choices() function from the random module to perform a random selection with probabilities based on the provided weights.”

20. Sketch out a proof that a k-means clustering algorithm will converge in a finite number of steps.

K-means clustering has various applications at Google such as image segmentation and improving search algorithms. Testing how nuanced your understanding is ensures the interviewers you can optimally implement them.

How to Answer

The key is to explain why the algorithm must converge in a finite number of steps based on the algorithm’s properties, such as the finite number of data points and cluster assignments.


“In k-means clustering, the goal is to partition ‘n’ data points into ‘k’ clusters in which each data point belongs to the cluster with the nearest mean. This process minimizes the within-cluster sum of squares. The algorithm iteratively assigns each data point to the nearest cluster based on the mean of the points in the cluster and then recalculates the means. These steps are repeated until the assignments no longer change.

The reason k-means clustering converges in a finite number of steps is due to the finite nature of the data set and the number of possible ways to partition these points into clusters. Each iteration of the algorithm refines the cluster assignments, making the total sum of distances from points to their respective cluster centers strictly non-increasing. Since there’s a lower bound and since the data set is finite, the algorithm must converge to a solution where subsequent iterations no longer change cluster assignments. This results in a stable configuration of clusters.”

How to Prepare for a Machine Learning Interview at Google

Here are some tips to help you excel in your Google interview.

Tailor Your Resume

Understand the job description clearly and prepare your resume accordingly. The resume screening determines whether you’ll make it to the interview process, so highlight your work experience and skills in line with what the recruiter wants to see.

Study the Company and Role

Understand the specific ML applications and technologies Google uses.

Research Google’s recent news, updates, values, and business challenges. Understanding the company’s culture and strategic goals will allow you to present yourself better and determine whether they are a good fit for you. You can also read Interview Query members’ experiences on our discussion board for insider tips and first-hand information.

Understand the Fundamentals

This interview will examine your acumen in machine learning and programming in depth. Be clear on core machine learning algorithms, data structures, and their applications, especially in the context of Google’s business use cases. Stay abreast of recent trends and news in ML and AI.

Our top tips: Have a clear overview of each step of the process and tailor your preparation accordingly. Prepare a script for case study and design rounds. It is crucial to have a strategy to get back on track if your answers get derailed or if you spend too much time on one subtopic. Practice time management, ask clarifying questions, and double-check your solutions beforehand.

To get more hands-on practice, refer to our handy guide on popular machine learning projects here and here, or test your algorithm knowledge here. You can also practice Python-based ML questions and check out our top ML interview questions.

Highlight Your Soft Skills

Soft skills like collaboration, effective communication, and flexibility are paramount to succeeding in any job, especially in a dynamic work environment like Google.

To test your current preparedness for the interview process and improve your communication skills, try a mock interview. Participating in multiple mock interviews is particularly useful for design rounds.


What is the average salary for a machine learning engineer role at Google?


Average Base Salary


Average Total Compensation

Min: $76K
Max: $232K
Base Salary
Median: $169K
Mean (Average): $166K
Data points: 200
Min: $8K
Max: $508K
Total Compensation
Median: $219K
Mean (Average): $227K
Data points: 199

View the full Machine Learning Engineer at Google salary guide

The average base salary for a machine learning engineer at Google is US$165,749, higher than the average base compensation for a data scientist in the US, which is around US$148,747.

What other companies besides Google’s machine learning engineer role can I apply for?

You can apply to similar roles in other MAANG companies. We have interview guides for Meta, Apple, Amazon, and Netflix.

For insights on other tech jobs, you can read more on our Company Interview Guides page.

Are there job postings for Google machine learning roles on Interview Query?

You can visit our job portal. There, you can sort the list by team, location preference, and your current skillsets and apply for your desired role. Even if you don’t have 100% of the requirements for a job, still apply since many skills can be learned on the job. Brush up on your fundamentals, be confident, and you should be good to go.


Succeeding in a Google machine learning interview requires a strong foundation in ML algorithms, a winning interview strategy, and extensive prep work.

Understanding Google’s experimentation-driven culture and thoroughly preparing with both technical and behavioral questions will be key to success. If you need more in-depth preparation, you can explore our tailored learning path in modeling and machine learning.

For other data-related roles at Google, consider exploring our guides for business analystsengineersscientists, and other positions in our main Google interview guide.

We wish you the best in your journey to landing a fulfilling role at Google!