Expedia (IT) Machine Learning Engineer interview questions & guide 2026

The questions below represent the patterns observed in recent Expedia (IT) interview cycles. While the specific technical focus may shift depending on your team—ranging from ad bidding to search optimization—the core expectation is a deep, practical understanding of machine learning foundations and their application in production environments.

Machine Learning Fundamentals

• What are the fundamental differences between XGBoost and Random Forest?
• How do you address the bias-variance tradeoff when adding data or reducing features?
• How do you handle class imbalance in datasets, specifically in high-stakes scenarios like fraud detection?

Preparation for Expedia (IT) requires a shift from theoretical knowledge to applied engineering. You must demonstrate that you can write production-grade code, not just prototype models.

• Technical Proficiency – You will be evaluated on your ability to implement algorithms from first principles. Expect to write code in a live environment or during an assessment; proficiency in Python and standard libraries is essential.
• Problem-Solving Structure – Interviewers look for how you break down complex, ambiguous problems. When faced with a case study, articulate your thought process regarding feature selection, model choice, and validation metrics before jumping into implementation.
• Systematic Thinking – Beyond the model, consider the infrastructure. Demonstrate your understanding of MLOps, including how models are deployed, monitored, and retrained in a production environment.
• Cultural Alignment – Expedia Group values collaboration and innovation. Be prepared to explain how your work impacts the user journey and how you communicate technical risks to non-technical stakeholders.

The interview process at Expedia (IT) is designed to test both your technical depth and your ability to function within a fast-paced, collaborative team. You should expect a rigorous assessment of your coding skills, your understanding of model architecture, and your ability to communicate complex technical concepts.

This timeline illustrates the progression from initial technical screening to multi-round interviews. Use this structure to pace your preparation, ensuring you have refreshed your knowledge of both core algorithms and system design principles before your final rounds.

Machine Learning Theory

Understanding the "why" behind the algorithms is essential. You must be able to compare models and explain the mathematical intuition behind parameter selection and optimization techniques.

Be ready to go over:

• Model Comparison – Deep dive into the mechanics of tree-based models versus linear models.
• Optimization – Understanding gradient-based methods and their convergence properties.

As a Machine Learning Engineer, your primary objective is to build and maintain high-performance ML pipelines. You will spend a significant portion of your time on data engineering, ensuring that the data ingested from ad clicks, impressions, and user searches is clean and ready for training.

You will work closely with ML Scientists to transition models from research to production. This involves optimizing inference for low-latency systems and implementing robust MLOps workflows. You will also collaborate with product managers to ensure that your models directly contribute to marketplace efficiency and revenue growth.

• Must-have skills – Proficiency in Python, experience with PyTorch or TensorFlow, and a solid grasp of SQL and Spark for large-scale data manipulation.
• Nice-to-have skills – Experience with cloud infrastructure (specifically AWS), familiarity with Databricks, and a background in ad-tech or marketplace platforms.
• Experience – A minimum of 3 years of industry experience is standard for this level, with a focus on end-to-end model deployment.

Q: How long should I spend preparing for the online assessment? A: Dedicate at least 3–5 days to practicing case studies similar to those found on data science platforms. Focus on feature engineering and model explainability, as these are frequently tested.

Q: Is there a heavy emphasis on system design? A: Yes, particularly for senior roles. You should be prepared to discuss how you would architect an ML system that handles millions of requests with low latency.

Q: What is the best way to prepare for the behavioral rounds? A: Use the STAR method (Situation, Task, Action, Result) to frame your experiences. Focus on stories that highlight your ability to collaborate across teams and handle technical ambiguity.

• Prioritize Code Quality: Even in assessments, write clean, documented code. Treat your submission as if it were being pushed to a production repository.
• Be Transparent: If you don't know an answer, explain your thought process and how you would go about finding the solution.
• Understand the Business: Research how Expedia Group makes money (e.g., ad-tech, booking commissions) to provide context-aware answers.

Preparing for a Machine Learning Engineer role at Expedia (IT) is a rigorous but rewarding endeavor. By mastering the core fundamentals of machine learning, sharpening your coding skills, and focusing on the practical application of models in production, you position yourself as a strong candidate for this high-impact role.

Leverage the insights provided here to structure your study plan. Remember that your ability to explain your technical decisions is just as important as the code you write. With a strategic approach and a focus on both engineering and science, you are well-prepared to take the next step in your career at Expedia Group.

The compensation data above provides a benchmark for the role. Use this to understand the market positioning for the Machine Learning Engineer position and to prepare for potential salary discussions during the final stages of your interview process.