Zillow Machine Learning Engineer Interview Questions + Guide in 2025

Zillow, a leader in the real estate technology industry, is on a mission to empower individuals to unlock life's next chapter through innovative digital solutions.

As a Machine Learning Engineer at Zillow, you will be at the forefront of developing AI-powered experiences that serve millions of customers each month. Your key responsibilities will include collaborating with applied scientists, software developers, and other machine learning engineers to design, prototype, and implement machine learning applications that solve complex customer problems. The role requires proficiency in machine learning frameworks, experience with large-scale datasets, and hands-on capabilities in deploying algorithms into production. You will be expected to have a strong understanding of natural language processing and be able to articulate your knowledge through practical examples.

Ideal candidates possess a growth mindset, are eager to learn new techniques, and can navigate ambiguous challenges with innovative solutions. You should have experience in using programming languages like Python or PySpark, as well as familiarity with machine learning tools such as PyTorch, XGBoost, and scikit-learn. Your ability to communicate effectively with cross-functional teams and lead projects will be critical to your success in this role.

This guide will help you prepare for your interview by providing insights into the skills and competencies that are emphasized at Zillow, equipping you to articulate your experience and demonstrate your fit for the Machine Learning Engineer position.

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at Zillow. The interview process will likely focus on your past experiences, technical skills, and problem-solving abilities in machine learning and data structures. Be prepared to discuss your projects in detail, as well as demonstrate your coding skills and understanding of machine learning concepts.

Experience and Background

1. Can you describe a machine learning project you worked on? What challenges did you face, and how did you overcome them?

This question aims to assess your practical experience and problem-solving skills in machine learning.

How to Answer

Discuss a specific project, focusing on the challenges you encountered and the solutions you implemented. Highlight your role in the project and the impact of your contributions.

Example

“In my last project, I developed a predictive model for housing prices. One major challenge was dealing with missing data. I implemented various imputation techniques and ultimately decided on a combination of mean and median imputation based on the data distribution. This approach improved the model's accuracy significantly.”

Machine Learning Concepts

2. What machine learning algorithms are you most familiar with, and when would you use each?

This question tests your knowledge of different algorithms and their applications.

How to Answer

Provide a brief overview of several algorithms, explaining their strengths and weaknesses, and the types of problems they are best suited for.

Example

“I am well-versed in algorithms like linear regression for continuous outcomes, decision trees for classification tasks, and clustering algorithms like K-means for unsupervised learning. For instance, I would use linear regression when the relationship between variables is linear and decision trees when interpretability is crucial.”

3. How do you handle overfitting in your models?

This question evaluates your understanding of model performance and generalization.

How to Answer

Discuss techniques you use to prevent overfitting, such as cross-validation, regularization, or pruning.

Example

“To combat overfitting, I typically use cross-validation to ensure my model generalizes well to unseen data. Additionally, I apply regularization techniques like L1 and L2 regularization to penalize overly complex models, which helps maintain a balance between bias and variance.”

Coding and Technical Skills

4. Can you write a function to calculate cosine similarity between two vectors?

This question assesses your coding skills and understanding of similarity measures.

How to Answer

Explain the concept of cosine similarity briefly before writing the function. Be prepared to discuss edge cases and optimizations.

Example

“Cosine similarity measures the cosine of the angle between two vectors. Here’s a simple implementation in Python: I would use NumPy to handle the vector operations efficiently.”

5. Describe how you would implement a machine learning model in a production environment.

This question evaluates your understanding of deployment processes and best practices.

How to Answer

Outline the steps you would take, including model selection, testing, monitoring, and updating the model in production.

Example

“I would start by selecting the best-performing model based on validation metrics. After thorough testing in a staging environment, I would deploy it using a CI/CD pipeline. Post-deployment, I would monitor the model's performance and set up alerts for any significant deviations, allowing for timely updates or retraining as needed.”

Data Structures and Algorithms

6. How would you find the least common ancestor of two nodes in a binary tree?

This question tests your knowledge of data structures and algorithms.

How to Answer

Explain the approach you would take, including any relevant algorithms or data structures.

Example

“I would use a recursive approach to traverse the tree. Starting from the root, I would check if the current node matches either of the target nodes. If it does, I return that node. If I find both nodes in the left and right subtrees, the current node is their least common ancestor.”

7. Can you explain the difference between a stack and a queue?

This question assesses your understanding of fundamental data structures.

How to Answer

Define both data structures and explain their use cases.

Example

“A stack is a Last In First Out (LIFO) structure, where the last element added is the first to be removed, making it ideal for scenarios like function call management. A queue, on the other hand, is a First In First Out (FIFO) structure, where the first element added is the first to be removed, which is useful for scheduling tasks.”

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

Prepare to Discuss Your Projects in Detail

Zillow places a strong emphasis on understanding your past experiences and projects. Be ready to discuss the challenges you faced, the models you chose, and the reasoning behind your decisions. Highlight the pros and cons of your chosen models and the outcomes of your projects. This will not only demonstrate your technical expertise but also your ability to reflect on your work critically.

Brush Up on Machine Learning Fundamentals

Given the role's focus on applied machine learning, ensure you have a solid grasp of key concepts such as overfitting, bias-variance tradeoff, and various machine learning algorithms. Be prepared to explain these concepts clearly and concisely, as interviewers may probe deeper into your understanding. Familiarize yourself with the latest trends in machine learning, especially in areas relevant to Zillow's AI initiatives.

Expect Coding Challenges

Be prepared for coding assessments that may involve writing functions or solving problems using machine learning methods. Practice coding problems that require you to implement algorithms from scratch, as well as those that test your understanding of data structures. Familiarity with platforms like HackerRank can be beneficial, as you may encounter similar formats during the interview process.

Communicate Effectively

Zillow values strong communication skills, especially when collaborating with cross-functional teams. Be prepared to explain your thought process clearly and to educate others on your methods and findings. Practice articulating complex technical concepts in a way that is accessible to non-experts, as this will demonstrate your ability to work collaboratively in a team environment.

Be Ready for Behavioral Questions

Expect behavioral questions that assess your fit within Zillow's culture. Reflect on your past experiences and be ready to share examples that showcase your problem-solving skills, adaptability, and teamwork. Zillow appreciates candidates who embody a growth mindset and can navigate ambiguity, so be prepared to discuss how you've approached challenges in the past.

Stay Informed About Company Culture

Zillow has a strong commitment to innovation and equity. Familiarize yourself with their mission and values, and think about how your personal values align with theirs. This understanding will not only help you answer questions more effectively but also allow you to ask insightful questions about the company culture and team dynamics.

Prepare for a Range of Interview Styles

Interviews at Zillow may vary in style, from technical assessments to behavioral interviews. Be adaptable and ready to switch gears as needed. If an interviewer seems to focus on areas outside of machine learning, such as data structures, remain calm and use the opportunity to showcase your versatility and problem-solving skills.

Follow Up Professionally

After your interview, consider sending a thank-you email to express your appreciation for the opportunity and to reiterate your interest in the role. This can help you stand out and leave a positive impression, especially in a competitive hiring environment.

By following these tips and preparing thoroughly, you'll position yourself as a strong candidate for the Machine Learning Engineer role at Zillow. Good luck!

The interview process for a Machine Learning Engineer at Zillow is structured to assess both technical and behavioral competencies, ensuring candidates are well-suited for the collaborative and innovative environment of the company. The process typically consists of several key stages:

1. Initial Recruiter Call

The first step is a phone call with a recruiter, which usually lasts about 30 minutes. During this conversation, the recruiter will review your resume, discuss your past experiences, and gauge your interest in the role. They may also inquire about your visa status and relocation preferences. This is an opportunity for you to ask questions about the company culture and the specifics of the Machine Learning Engineer role.

2. Technical Assessment

Following the recruiter call, candidates are often required to complete a technical assessment, which may be conducted through platforms like HackerRank. This assessment typically includes programming tasks that test your coding skills and understanding of machine learning concepts. You may be given a set timeframe to complete the tasks, which can involve implementing algorithms or solving data manipulation problems.

3. Phone Interview with a Machine Learning Engineer

If you pass the technical assessment, the next step is a phone interview with a Machine Learning Engineer. This interview usually begins with a discussion about your previous projects and experiences. Expect to dive deep into the technical details of your work, including the models you chose, the challenges you faced, and the outcomes of your projects. You may also be asked to solve coding problems in real-time, focusing on data structures and algorithms, which are critical for the role.

4. Onsite or Virtual Interview

The final stage is typically an onsite or virtual interview, which may consist of multiple rounds with different team members, including applied scientists and software developers. These interviews will cover a range of topics, including machine learning fundamentals, statistical methods, and practical applications of algorithms. You should be prepared for both technical questions and behavioral assessments, as the interviewers will be looking for candidates who can collaborate effectively within a team.

Throughout the interview process, it is essential to demonstrate not only your technical expertise but also your ability to communicate complex ideas clearly and work collaboratively with others.

As you prepare for your interviews, consider the types of questions that may arise in each of these stages.

1. How would you improve Google Maps?

As a PM on Google Maps, what specific features or enhancements would you implement to improve the user experience?

2. How would you determine whether or not implementing a payment feature in Facebook Messenger is a good business decision?

Facebook is considering adding a payment feature to Messenger. What criteria and analyses would you use to assess if this is a beneficial business move?

3. How would you investigate a 10% drop in usage on Google Docs?

If Google Docs experiences a 10% decline in usage, what steps and methods would you take to identify the cause?

4. Create a function rectangle_overlap to determine if two rectangles overlap.

You are given two rectangles a and b each defined by four ordered pairs denoting their corners on the x, y plane. Write a function rectangle_overlap to determine whether or not they overlap. Return True if so, and False otherwise.

5. Develop a function rain_days to calculate the probability of rain on the nth day after today.

The probability that it will rain tomorrow is dependent on whether or not it is raining today and whether or not it rained yesterday. Given that it is raining today and that it rained yesterday, write a function rain_days to calculate the probability that it will rain on the nth day after today.

6. What’s the difference between Lasso and Ridge Regression?

Explain the key differences between Lasso and Ridge Regression, focusing on their regularization techniques and how they handle coefficients.

7. What kind of model did the co-worker develop for loan approval?

Identify the type of model used for determining loan approval based on customer inputs.

8. How would you evaluate the suitability of a decision tree for predicting loan repayment?

Describe the criteria and methods you would use to determine if a decision tree algorithm is appropriate for predicting loan repayment.

9. How does random forest generate the forest and why use it over logistic regression?

Describe the process by which random forest generates its ensemble of trees and explain the advantages of using random forest over logistic regression.

10. How would you interpret coefficients of logistic regression for categorical and boolean variables?

Explain the interpretation of logistic regression coefficients when dealing with categorical and boolean variables.

11. How would you handle a right-skewed distribution when predicting real estate home prices?

If building a model to predict real estate home prices and the distribution is right-skewed, should you take any actions? If so, what steps should you take?

You should plan to brush up on any technical skills and try as many practice interview questions and mock interviews as possible. A few tips for acing your Zillow machine learning engineer interview include:

• Preparation is Key: Make sure you thoroughly prepare for both software engineering and machine learning concepts. Practice coding problems on Interview Query and focus on data structures, algorithms, and typical ML use-cases.
• Know Zillow Products: Understand the various products and services offered by Zillow. Be prepared to discuss how your skills can add value and improve Zillow’s AI and ML offerings.
• Behavioral Questions: Be ready to answer behavioral questions that reflect Zillow’s commitment to a culture of innovation, collaborative problem-solving, and customer focus.

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

What skills are required to work as a Machine Learning Engineer at Zillow?

To work as a Machine Learning Engineer at Zillow, you should have robust programming skills, particularly in languages like Python, knowledge in statistics, experience with machine learning frameworks (e.g., PyTorch, TensorFlow), and hands-on experience deploying models in production environments. Familiarity with large-scale data processing and cloud services such as AWS is also beneficial.

What is the company culture like at Zillow?

Zillow fosters a culture of innovation, collaboration, and diversity. They are deeply committed to equity and belonging, supporting employees in achieving a balanced and flexible work life. The company has received recognition for its employee experience, including accolades from Glassdoor and TIME.

Interviewing for a Machine Learning Engineer position at Zillow offers many experiences. While the role promises exciting opportunities to work on innovative AI products and solutions, securing the position can be challenging. What stands out is the variety of interview processes, ranging from coding challenges and technical deep dives to behavioral and project-based discussions. Some candidates reported encounters with less-than-ideal interviewers and mismatched expectations, underscoring the importance of thorough preparation. On the positive side, Zillow is a great place to push the boundaries of applied machine learning.

To enhance your chances of success, it’s crucial to be well-prepared. If you want more insights into Zillow’s interview process, check out our Zillow Interview Guide on Interview Query, where we’ve compiled many potential questions and detailed guides for various roles.

Good luck with your interview!