Zillow Machine Learning Engineer Interview Questions + Guide in 2024

Zillow is a leading real estate and rental marketplace dedicated to empowering consumers with data, inspiration, and knowledge to make informed property decisions. Through its intuitive platform, Zillow connects buyers, sellers, renters, and real estate professionals, fostering transparency and providing a seamless experience in the real estate market.

In this guide, we’ll tackle how they conduct their machine learning interviews, along with commonly asked Zillow machine learning engineer interview questions to help you prepare better.

The interview process usually depends on the role and seniority. However, you can expect the following on a Zillow machine learning engineer interview:

Recruiter/Hiring Manager Call Screening

If your CV is shortlisted, a Zillow Talent Acquisition team recruiter will contact you to verify details like your experiences, skills, and general interest in the role. The recruiter may ask behavioral questions and discuss your availability, visa status, and relocation preferences. This call usually lasts about 30 minutes.

Technical Assessment

Following the initial recruiter call, you will likely receive a technical assessment. Typically, this could be a Hackerrank assessment, which you are given a specific time frame to complete (often 40 hours). These assessments may include moderately complex programming tasks that test your coding skills in languages commonly used by Zillow, like Python or Java.

Technical Virtual Interview

After completing the technical assessment, you will be invited to a technical interview with a Machine Learning Engineer from Zillow. This virtual interview will involve questions about past experiences, projects, and machine-learning methods. You may be asked to write code to solve a case question, often related to data manipulation, ML algorithms, and their application.

Questions could explore your choice of models, the pros and cons, how you overcame challenges, and the outcomes of your projects. Coding challenges on data structures and algorithms frequently encountered on platforms like Leetcode might also arise.

Onsite Interview Rounds

Once you clear the technical virtual interview, you will be invited to the onsite interview rounds. Given the remote flexibility, these might also be conducted virtually. These multiple interview rounds will involve in-depth discussions on your technical skills, including programming, data pipelines, ML modeling, and possibly an ML-related take-home assignment presentation.

Typically, interviews at Zillow vary by role and team, but commonly Machine Learning Engineer interviews follow a fairly standardized process across these question topics.

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!