Realtor.Com Machine Learning Engineer Interview Questions + Guide 2024

Realtor.com is a leading real estate platform offering the most comprehensive and accurate coverage of property listings. With a mission to simplify the home buying, selling, and renting processes, Realtor.com aims to enhance everyone’s real estate experience.

As a Machine Learning Engineer at Realtor.com, you’ll be part of a team that processes terabytes of data daily to drive insights and decisions for millions of users. Your role will encompass data retrieval, exploratory analysis, model development, and deployment, making significant contributions to improving consumer experiences and business decisions.

In this guide, we’ll explore the interview process for the machine learning engineer position at Realtor.com, detailing the stages and types of Realtor.com machine learning engineer interview questions you might encounter. Let’s dive in!

Recruiter/Hiring Manager Call Screening

If your application makes it through the initial round, a recruiter from Realtor.com’s Talent Acquisition Team will contact you to verify your experiences and assess your skill set. During this screening call, which typically lasts around 30 minutes, expect to answer behavioral questions and discuss your background. You’ll also get information on the role, including location, compensation range, and project details.

The recruiter might cover the following:

• Why Realtor.com?
• What are your salary expectations?
• Background and previous roles.
• High-level summary of technical experience.

Technical Virtual Interview

Passing the recruiter call will lead to a technical interview, generally conducted virtually in a video conference format. This round includes coding questions typically completed in an IDE in front of the interviewer. Based on interview feedback, questions may involve SQL, Python, and specific challenges related to the role, such as window functions in SQL or designing a system.

Potential topics and sample questions are:

• SQL: Finding the median within subgroups and window functions.
• Python: Anagram Finder, reversing a string, closures.
• System Design: Designing systems like Lyft or integrated counting systems.

Depending on the role’s seniority, you might be assigned take-home assignments or case-based problems as part of this round.

Onsite Interview Rounds

If you pass the technical virtual interview, the next step involves onsite interviews, which can last from a few hours to a full day. The onsite rounds generally consist of multiple 30-—to 45-minute sessions with various team members and a follow-up meeting with the hiring manager.

Expect the following:

• First Interview: Discussing past experiences and your resume.
• Technical Rounds: In-depth SQL and Python questions, potentially some coding challenges.
• Behavioral Interviews: More conversational, focusing on cultural fit.
• Case Presentation: Particularly for senior roles, presenting solutions to hypothetical scenarios or past projects.

Interviewers may include engineers, product managers, directors, and other senior staff, assessing both technical and soft skills.

Typically, interviews at Realtor.Com vary by role and team, but commonly machine learning engineer interviews follow a fairly standardized process across these question topics.

1. How would you explain what a p-value is to someone who is not technical?

Explain the concept of a p-value in simple terms to someone without a technical background.

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

You are building a model to predict home prices in a city and notice the distribution is right-skewed. Should you take any action? If so, what should you do?

2.5 Bonus: How should you handle a left-skewed target distribution?

If the target distribution is heavily left-skewed, what steps should you take?

3. What considerations should be made when testing hundreds of hypotheses with many t-tests?

When conducting multiple t-tests, you need to consider the increased risk of Type I errors (false positives). Implement methods like the Bonferroni correction or False Discovery Rate (FDR) to control for this risk and ensure the validity of your results.

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

Explain how random forest generates multiple decision trees and why it might be preferred over logistic regression for certain tasks.

5. How do we deal with missing square footage data in housing price predictions?

You have 100K sold listings with 20% missing square footage data. Describe methods to handle the missing data to construct a reliable model.

6. How would you combat overfitting in tree-based classification models?

When training a classification model, explain strategies to prevent overfitting, particularly in tree-based models.

7. Does increasing the number of trees in a random forest always improve accuracy?

Discuss whether sequentially increasing the number of trees in a random forest model will continuously improve its accuracy.

8. How do you implement k-means clustering in Python from scratch?

Given a two-dimensional NumPy array data_points, number of clusters k, and initial centroids initial_centroids, write a Python function to perform k-means clustering and return the cluster assignment for each data point.

9. Write a SQL query to select the 2nd highest salary in the engineering department.

Write a SQL query to select the 2nd highest salary in the engineering department. If more than one person shares the highest salary, the query should select the next highest salary.

10. Create a function precision_recall to calculate precision and recall metrics from a 2-D matrix.

Given a 2-D matrix P of predicted values and actual values, write a function precision_recall to calculate precision and recall metrics. Return the ordered pair (precision, recall).

11. Write a SQL query to select the top 3 departments with at least ten employees and rank them by the percentage of employees making over 100K.

Given an employees and departments table, select the top 3 departments with at least ten employees and rank them according to the percentage of their employees making over 100K in salary.

12. Write a function traverse_count to determine the number of paths in an (n\times n) grid.

Given an integer (n), write a function traverse_count to determine the number of paths from the top left corner of an (n\times n) grid to the bottom right. You may only move right or down.

13. Develop a function is_subsequence to check if one string is a subsequence of another.

Given two strings, string1 and string2, write a function is_subsequence to find out if string1 is a subsequence of string2.

Here are some proven tips to help you prepare for your interview at Realtor.com:

• Familiarize with SQL Window Functions: Many candidates reported encountering SQL window functions and similar complex queries. Make sure to practice these extensively.

• Prepare for Mixed Technical Challenges: Expect a range of questions from basic Python and JavaScript to more complex system designs and algorithm problems. Practicing a variety of coding problems can be beneficial.

• Stay Professional and Courteous: Despite mixed reviews about some interviewers’ professionalism and conduct, stay courteous and present your experience confidently. Reflect Realtor.com’s values even during challenging moments.

• Do Mock Interviews: Getting feedback from someone else is a great way to learn what you need to improve and your strong points. Consider trying our mock interview platform or AI interview to get help.

By following these and preparing thoroughly, you can enhance your chances of securing a position at Realtor.com and joining a diverse and innovative team driving the future of real estate technology.

What is the average salary for a Machine Learning Engineer at Realtor.Com?

What can I expect from the work culture at Realtor.com?

At Realtor.com, the work culture balances creativity and innovation with in-person collaboration. Employees generally work three or more days in the office, fostering a rich culture of teamwork and closer connections. The environment is welcoming, inclusive, and conducive to professional growth.

How does Realtor.com support diversity and inclusion in the workplace?

Realtor.com is an Equal Opportunity Employer, committed to providing a diverse and inclusive environment. They do not discriminate based on age, race, gender, sexual orientation, or any other protected characteristic. The company also offers reasonable accommodations for qualified disabled individuals.

What kinds of projects will I work on as a Machine Learning Engineer at Realtor.com?

You will work on creating data-driven and machine-learning-based features to enhance the consumer experience for home shoppers, renters, and sellers. This includes building ETL pipelines, developing predictive models, conducting A/B tests, and deploying models to production environments on AWS. The role involves significant cross-functional collaboration with product and engineering teams.

The process of interviewing for a Machine Learning Engineer position at Realtor.com can be a rollercoaster, but it’s essential to stay poised and prepared. Despite some challenges like close-ended questions and occasional recruiter inefficiencies, there’s also ample opportunity for positive experiences, including engaging technical rounds and supportive interactions with hiring managers.

By paying attention to the specifics of the role and familiarizing yourself with tools like SQL and Python, you’ll stand a much better chance of showcasing your fit for the team. Don’t let the occasional hiccup deter you—focus on demonstrating your expertise in data and machine learning, and remember, the team at Realtor.com is looking for someone who can contribute meaningfully to their mission of transforming the real estate industry.

For those aiming to dive deeper into the interview tips and tricks, consider exploring additional resources tailored for Machine Learning Engineer role preparations. Good luck, and may you find your dream job and make a significant impact at Realtor.com!