Expedia, Inc. Machine Learning Engineer Interview Questions + Guide in 2025

Expedia Group is a global leader in the travel industry, dedicated to connecting millions of travelers to unforgettable experiences and fostering lasting connections through cutting-edge technology.

As a Machine Learning Engineer at Expedia, you will play a pivotal role in harnessing advanced machine learning techniques to enhance travel personalization and optimize customer engagement. Key responsibilities include collaborating with cross-functional teams to deploy machine learning models into production, developing efficient data pipelines, and ensuring the integrity and scalability of these systems. A strong understanding of machine learning frameworks, data processing methodologies, and cloud services will be essential, as you will work with vast datasets to extract actionable insights that directly impact the travel experience. The ideal candidate is not only technically proficient but also possesses excellent communication skills to present complex ideas clearly to both technical and non-technical stakeholders.

This guide will equip you with the necessary insights and strategies to effectively prepare for your interview at Expedia, ensuring you stand out as a top candidate for the Machine Learning Engineer role.

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

1. Initial Contact

The journey often begins with an initial outreach from a recruiter, which may occur through networking events or direct applications. This stage involves a brief conversation to gauge your interest in the role and to discuss your background, skills, and career aspirations. The recruiter will also provide insights into the company culture and the specifics of the Machine Learning Engineer position.

2. Behavioral Interview

Following the initial contact, candidates usually participate in a behavioral interview. This round focuses on understanding your work approach, problem-solving abilities, and how you align with Expedia's values. Expect questions that explore your past experiences, teamwork, and how you handle challenges in a professional setting. This stage is crucial for assessing cultural fit within the organization.

3. Technical Assessment

Candidates who successfully navigate the behavioral interview will move on to a technical assessment. This may involve a coding challenge or a technical interview where you will be asked to solve problems related to machine learning concepts, algorithms, and data manipulation. You might be required to demonstrate your proficiency in programming languages such as Python, as well as your understanding of machine learning frameworks and tools.

4. Onsite Interviews

The final stage typically consists of onsite interviews, which may be conducted virtually. This phase includes multiple rounds with different team members, including Machine Learning Engineers and Data Scientists. Each interview lasts around 45 minutes and covers a mix of technical questions, case studies, and discussions about your previous projects. You will be expected to articulate your thought process clearly and demonstrate your ability to collaborate effectively with cross-functional teams.

5. Final Review

After the onsite interviews, the hiring team will review all candidate performances and make a decision. This may involve discussions about your technical skills, cultural fit, and potential contributions to the team. If selected, you will receive an offer that includes details about compensation and benefits.

As you prepare for your interview, consider the specific questions that may arise during this process.

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

Understand the Company Culture

Expedia values a diverse and inclusive work environment, where differences are celebrated and collaboration is key. Familiarize yourself with their core values and leadership agreements, as these will guide your interactions during the interview. Be prepared to discuss how your personal values align with the company’s mission to make travel accessible and enjoyable for everyone.

Prepare for Behavioral Questions

Expect a behavioral round that assesses your work approach and cultural fit. Reflect on your past experiences and be ready to share specific examples that demonstrate your problem-solving skills, teamwork, and adaptability. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you convey the impact of your contributions clearly.

Brush Up on Technical Skills

Given the technical nature of the Machine Learning Engineer role, ensure you are well-versed in relevant programming languages (especially Python), machine learning frameworks (like TensorFlow or PyTorch), and data processing tools (such as Spark). Be prepared to discuss your experience with building and deploying machine learning models, as well as your understanding of data pipelines and ETL processes.

Showcase Your Problem-Solving Abilities

During technical interviews, you may be asked to solve real-world problems related to machine learning. Practice articulating your thought process as you work through these problems. Highlight your ability to identify business challenges and propose data-driven solutions, demonstrating your understanding of how machine learning can enhance customer experiences in the travel industry.

Communicate Effectively

Expedia emphasizes the importance of clear communication, both with technical and non-technical stakeholders. Practice explaining complex technical concepts in simple terms. Be prepared to present your ideas and results in a compelling manner, as you may need to influence decisions and drive action based on your findings.

Engage with Your Interviewers

Show genuine interest in the team and the projects they are working on. Ask insightful questions about their current challenges and how your skills can contribute to their goals. This not only demonstrates your enthusiasm for the role but also helps you assess if the team dynamics and projects align with your career aspirations.

Follow Up Professionally

After the interview, send a thoughtful thank-you email to your interviewers. Express your appreciation for the opportunity to learn more about the team and reiterate your excitement about the potential to contribute to Expedia’s mission. This small gesture can leave a lasting impression and reinforce your interest in the position.

By following these tips, you can position yourself as a strong candidate for the Machine Learning Engineer role at Expedia. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at Expedia, Inc. The interview process will likely assess both your technical skills and your ability to work collaboratively within a team. Be prepared to discuss your experience with machine learning models, data processing, and your approach to problem-solving in a business context.

Machine Learning

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

Understanding the fundamental concepts of machine learning is crucial. Be clear about the definitions and provide examples of each type.

How to Answer

Discuss the key differences, such as the presence of labeled data in supervised learning versus the absence in unsupervised learning. Provide examples of algorithms used in each.

Example

“Supervised learning involves training a model on a labeled dataset, where the outcome is known, such as classification tasks using algorithms like logistic regression. In contrast, unsupervised learning deals with unlabeled data, aiming to find hidden patterns, like clustering with K-means.”

2. Describe a machine learning project you worked on from start to finish.

This question assesses your practical experience and ability to manage a project.

How to Answer

Outline the problem, your approach, the tools you used, and the outcome. Highlight your role in the project and any challenges you faced.

Example

“I worked on a project to predict customer churn for an e-commerce platform. I started by gathering and cleaning the data, then used Python and scikit-learn to build a logistic regression model. After validating the model, we implemented it in production, which led to a 15% reduction in churn rates.”

3. How do you handle overfitting in your models?

This question tests your understanding of model performance and evaluation.

How to Answer

Discuss techniques such as cross-validation, regularization, and pruning. Mention how you apply these methods in practice.

Example

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

4. What metrics do you use to evaluate the performance of a machine learning model?

This question gauges your knowledge of model evaluation.

How to Answer

Mention various metrics relevant to the type of problem (e.g., accuracy, precision, recall, F1 score for classification; RMSE, MAE for regression).

Example

“I typically use accuracy and F1 score for classification tasks to balance precision and recall. For regression, I prefer RMSE as it gives a clear indication of the model's prediction error in the same units as the target variable.”

5. Can you explain how a recommender system works?

Given Expedia's focus on travel, this question is particularly relevant.

How to Answer

Discuss collaborative filtering, content-based filtering, and hybrid approaches. Explain how these systems can enhance user experience.

Example

“A recommender system can use collaborative filtering to suggest items based on user behavior and preferences, while content-based filtering recommends items similar to those the user has liked in the past. A hybrid approach combines both methods to improve accuracy and user satisfaction.”

Data Engineering

1. Describe your experience with ETL processes.

This question assesses your data handling skills.

How to Answer

Explain the ETL process, tools you’ve used, and how you ensure data quality.

Example

“I have extensive experience with ETL processes using Apache Airflow for orchestration. I design pipelines that extract data from various sources, transform it for analysis, and load it into a data warehouse, ensuring data quality through validation checks at each stage.”

2. How do you optimize a data pipeline for performance?

This question evaluates your ability to enhance efficiency.

How to Answer

Discuss techniques like parallel processing, efficient data storage formats, and indexing.

Example

“To optimize data pipelines, I implement parallel processing using Spark, which allows for faster data processing. I also use efficient storage formats like Parquet and apply indexing to speed up query performance in our data warehouse.”

3. What tools do you use for data visualization?

This question checks your ability to communicate data insights.

How to Answer

Mention specific tools and how you use them to present data effectively.

Example

“I frequently use Tableau and Matplotlib for data visualization. Tableau allows me to create interactive dashboards for stakeholders, while Matplotlib is great for custom visualizations in Python scripts to analyze data trends.”

4. Explain how you would handle missing data in a dataset.

This question tests your data preprocessing skills.

How to Answer

Discuss various strategies such as imputation, removal, or using algorithms that handle missing values.

Example

“I handle missing data by first assessing the extent of the missingness. For small amounts, I might use mean or median imputation. If a significant portion is missing, I consider removing those records or using algorithms like KNN that can handle missing values effectively.”

5. How do you ensure data integrity and security in your projects?

This question evaluates your understanding of data governance.

How to Answer

Discuss practices like data validation, access controls, and encryption.

Example

“I ensure data integrity by implementing validation checks during data entry and processing. For security, I use role-based access controls to limit data access and encrypt sensitive data both at rest and in transit.”

Communication and Collaboration

1. How do you communicate complex technical concepts to non-technical stakeholders?

This question assesses your communication skills.

How to Answer

Explain your approach to simplifying technical jargon and using visuals.

Example

“I focus on using analogies and simple language to explain complex concepts. I also create visual aids like charts and graphs to help stakeholders understand the data and its implications without getting lost in technical details.”

2. Describe a time you had to work with a cross-functional team.

This question evaluates your teamwork skills.

How to Answer

Share an example that highlights collaboration and conflict resolution.

Example

“I collaborated with product managers and engineers on a project to enhance our search algorithm. We held regular meetings to align our goals and address any conflicts, which ultimately led to a successful implementation that improved user engagement by 20%.”

3. How do you prioritize tasks when working on multiple projects?

This question assesses your organizational skills.

How to Answer

Discuss your approach to task management and prioritization.

Example

“I prioritize tasks based on project deadlines and impact. I use tools like Trello to track progress and ensure that I focus on high-impact tasks first, while also allowing flexibility for urgent requests from stakeholders.”

4. Can you give an example of how you handled a disagreement within your team?

This question evaluates your conflict resolution skills.

How to Answer

Share a specific instance and how you navigated the disagreement.

Example

“In a previous project, there was a disagreement on the choice of algorithm. I facilitated a discussion where each team member presented their viewpoint, and we collectively evaluated the pros and cons. This collaborative approach led us to choose the best solution based on data-driven insights.”

5. How do you document your work and ensure knowledge sharing within the team?

This question assesses your commitment to collaboration and transparency.

How to Answer

Discuss your documentation practices and tools used.

Example

“I document my work using Confluence, where I outline processes, decisions made, and results achieved. I also encourage team members to contribute to the documentation, ensuring that knowledge is shared and accessible for future reference.”