Realtor.Com Machine Learning Engineer Interview Questions + Guide in 2025

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.

Realtor.com is a leading online real estate platform that connects buyers, sellers, and renters with real estate professionals and listings across the United States.

The Machine Learning Engineer role at Realtor.com is critical for leveraging data to enhance user experiences and improve service offerings on the platform. This position involves developing machine learning models and algorithms to analyze large datasets, enabling the company to provide personalized recommendations and insights to its users. Key responsibilities include designing and implementing machine learning systems, conducting experiments to optimize model performance, and collaborating with cross-functional teams to integrate these models into production environments.

Candidates should possess strong programming skills in Python, a solid understanding of algorithms and machine learning principles, and experience with SQL for data manipulation. Familiarity with statistical analysis and a knack for problem-solving are essential traits that will help a candidate excel in this role. Additionally, a passion for real estate technology and a commitment to enhancing user engagement align well with Realtor.com's values of innovation and customer-centricity.

This guide aims to equip you with the insights and knowledge needed to effectively demonstrate your skills and fit for the Machine Learning Engineer role during your interview process at Realtor.com. By understanding the key responsibilities and required skills, you can prepare targeted responses and showcase your expertise confidently.

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at Realtor.com. The interview process will likely assess your technical skills in machine learning, algorithms, and programming, as well as your problem-solving abilities and cultural fit within the company. Be prepared to discuss your past experiences, technical knowledge, and how you approach challenges in the field.

Machine Learning

1. Can you explain the difference between supervised and unsupervised learning?

Understanding the fundamental concepts of machine learning is crucial. Be clear and concise in your explanation, providing examples of each type.

How to Answer

Discuss the definitions of both supervised and unsupervised learning, highlighting the key differences in terms of labeled data and the types of problems they solve.

Example

“Supervised learning involves training a model on a labeled dataset, where the input data is paired with the correct output. For example, predicting house prices based on features like size and location. In contrast, unsupervised learning deals with unlabeled data, where the model tries to identify patterns or groupings, such as clustering customers based on purchasing behavior.”

2. Describe a machine learning project you have worked on. What challenges did you face?

This question assesses your practical experience and problem-solving skills in real-world applications.

How to Answer

Outline the project scope, your role, the challenges encountered, and how you overcame them. Use the STAR method to structure your response.

Example

“I worked on a project to predict customer churn for a subscription service. One challenge was dealing with imbalanced data. I implemented techniques like SMOTE to generate synthetic samples and improved the model's performance, ultimately reducing churn prediction errors by 20%.”

3. How do you evaluate the performance of a machine learning model?

This question tests your understanding of model evaluation metrics and their importance.

How to Answer

Discuss various metrics such as accuracy, precision, recall, F1 score, and ROC-AUC, and explain when to use each.

Example

“I evaluate model performance using metrics like accuracy for balanced datasets, but I prefer precision and recall for imbalanced datasets. For instance, in a fraud detection model, high precision is crucial to minimize false positives, while recall ensures we catch as many fraudulent cases as possible.”

4. What techniques do you use to prevent overfitting in your models?

This question assesses your knowledge of model generalization and techniques to improve it.

How to Answer

Mention techniques such as cross-validation, regularization, and pruning, and explain how they help in preventing overfitting.

Example

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

5. Can you explain the concept of feature engineering and its importance?

This question evaluates your understanding of data preprocessing and its impact on model performance.

How to Answer

Define feature engineering and discuss its role in improving model accuracy and interpretability.

Example

“Feature engineering is the process of selecting, modifying, or creating new features from raw data to improve model performance. It’s crucial because well-engineered features can significantly enhance the model's ability to learn patterns, leading to better predictions. For instance, creating interaction terms or aggregating features can reveal hidden relationships in the data.”

Algorithms

1. What is the difference between a decision tree and a random forest?

This question tests your knowledge of algorithms and their applications.

How to Answer

Explain the basic structure of both algorithms and their advantages and disadvantages.

Example

“A decision tree is a single tree structure that splits data based on feature values, making it easy to interpret. However, it can easily overfit. A random forest, on the other hand, is an ensemble of multiple decision trees, which improves accuracy and robustness by averaging their predictions, thus reducing overfitting.”

2. How would you implement a k-means clustering algorithm?

This question assesses your understanding of clustering algorithms and their implementation.

How to Answer

Outline the steps involved in the k-means algorithm, including initialization, assignment, and update steps.

Example

“To implement k-means clustering, I would first initialize k centroids randomly. Then, I would assign each data point to the nearest centroid based on Euclidean distance. After that, I would update the centroids by calculating the mean of the assigned points. This process repeats until the centroids stabilize.”

3. Explain the concept of gradient descent. How does it work?

This question evaluates your understanding of optimization techniques used in machine learning.

How to Answer

Describe the gradient descent algorithm and its purpose in minimizing loss functions.

Example

“Gradient descent is an optimization algorithm used to minimize the loss function in machine learning models. It works by calculating the gradient of the loss function with respect to the model parameters and updating the parameters in the opposite direction of the gradient. This process continues iteratively until convergence is achieved.”

4. What is the purpose of cross-validation in model training?

This question assesses your understanding of model validation techniques.

How to Answer

Discuss the importance of cross-validation in assessing model performance and preventing overfitting.

Example

“Cross-validation is used to evaluate a model's performance by partitioning the data into training and validation sets multiple times. This helps ensure that the model generalizes well to unseen data and reduces the risk of overfitting by providing a more reliable estimate of its performance.”

5. Can you describe a time when you had to optimize an algorithm for performance?

This question evaluates your practical experience with algorithm optimization.

How to Answer

Use the STAR method to describe the situation, the actions you took, and the results achieved.

Example

“I was tasked with optimizing a recommendation algorithm that was running too slowly. I analyzed the code and identified bottlenecks in the data processing steps. By implementing more efficient data structures and parallel processing, I reduced the algorithm's runtime by 50%, significantly improving user experience.”

The interview process for a Machine Learning Engineer at Realtor.com is structured to assess both technical skills and cultural fit within the team. It typically consists of several stages, each designed to evaluate different aspects of a candidate's qualifications and experience.

1. Initial Recruiter Call

The process begins with a phone screening conducted by a recruiter. This initial call usually lasts around 30 minutes and focuses on understanding your background, technical skills, and motivations for applying to Realtor.com. The recruiter may ask about your previous roles, relevant experiences, and your understanding of the company and its culture. This is also an opportunity for you to ask questions about the role and the team.

2. Technical Phone Screen

Following the recruiter call, candidates typically participate in a technical phone interview. This session is often led by a hiring manager or a senior engineer and lasts about 30 to 45 minutes. During this interview, you can expect to tackle coding challenges and technical questions related to machine learning concepts, algorithms, and programming languages such as Python and SQL. Be prepared to demonstrate your problem-solving skills and discuss your approach to various technical scenarios.

3. Onsite Interview

The onsite interview is a more comprehensive evaluation, usually consisting of multiple rounds with different team members. This stage can last several hours and may include a mix of technical assessments, coding challenges, and behavioral interviews. Candidates might face questions related to machine learning algorithms, statistical analysis, and practical applications of their skills in real-world scenarios. Additionally, there may be discussions about past projects and how you have applied machine learning techniques in those contexts.

4. Final Interview Round

In some cases, the final round may involve a presentation or case study where you are asked to solve a specific problem or present a project you have worked on. This round is designed to assess your ability to communicate complex ideas clearly and effectively, as well as your critical thinking and analytical skills. It may also include a discussion with higher-level management to evaluate your fit within the broader organizational structure.

As you prepare for your interview, it's essential to familiarize yourself with the types of questions that may arise during each stage of the process.

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.

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!