Criteo Machine Learning Engineer Interview Questions + Guide in 2025

Criteo (CRTO) specializes in delivering personalized performance online marketing through sophisticated machine learning at scale, making marketing campaign ROI transparent and measurable.

As a Machine Learning Engineer at Criteo, you will play a pivotal role in developing advanced marketing solutions tailored for the consumer packaged goods (CPG) sector. Your key responsibilities will include writing high-quality, maintainable code and mentoring other engineers while implementing and deploying scalable machine learning algorithms using technologies like Hadoop, MapReduce, Python, Scala, or Spark. You will also be tasked with building predictive models to analyze shopping behaviors, architecting system designs for scalability, and uncovering insights hidden within vast datasets. The ideal candidate will possess a robust background in software engineering, with at least 8 years of programming experience and significant expertise in big data processing and machine learning algorithms.

Criteo values innovation, teamwork, and a drive for problem-solving, making it essential for candidates to embody these traits. This guide will help you prepare thoroughly for your interview by providing insights into what to expect and how to align your responses with Criteo's core values and operational processes.

The interview process for a Machine Learning Engineer at Criteo is structured and involves multiple stages designed to assess both technical skills and cultural fit. Here’s a breakdown of the typical process:

1. Initial Screening

The process begins with an initial screening call, usually conducted by a recruiter. This call lasts about 30 minutes and focuses on understanding your background, motivations for applying, and basic qualifications for the role. Expect questions about your experience with machine learning, programming languages, and your interest in Criteo.

2. Technical Assessment

Following the initial screening, candidates typically undergo a technical assessment. This may involve an online coding test or a take-home assignment where you will be asked to solve problems related to data analysis, machine learning algorithms, or coding challenges. The assessment is designed to evaluate your proficiency in languages such as Python, Scala, or Java, as well as your understanding of machine learning concepts.

3. Technical Interviews

Candidates who pass the technical assessment will move on to one or more technical interviews. These interviews can be conducted via video call and usually last about an hour each. You will be asked to solve coding problems in real-time, often using a whiteboard or shared coding environment. Expect questions that test your knowledge of algorithms, data structures, and system design, particularly in the context of large-scale data processing and machine learning.

4. Design Interview

In addition to coding interviews, candidates may also participate in a design interview. This session focuses on your ability to architect scalable machine learning systems. You will be asked to discuss your approach to designing algorithms and systems that can handle large datasets, as well as your experience with tools like Hadoop and Spark.

5. Behavioral Interview

The final stage typically includes a behavioral interview, which assesses your fit within Criteo's culture. This interview may involve questions about your past experiences, teamwork, and problem-solving approaches. Using the STAR (Situation, Task, Action, Result) method to structure your responses can be beneficial.

6. Final Discussions

In some cases, candidates may have a final discussion with senior team members or management. This is an opportunity for you to ask questions about the team, projects, and company culture, while also allowing the interviewers to gauge your enthusiasm and alignment with Criteo's values.

As you prepare for your interview, be ready to tackle a variety of technical challenges and demonstrate your problem-solving skills, as well as your passion for machine learning and data-driven solutions.

Next, let’s delve into the specific interview questions that candidates have encountered during the process.

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

Understand the Technical Landscape

Given Criteo's emphasis on machine learning and big data, it's crucial to familiarize yourself with the technologies they use, such as Hadoop, MapReduce, Python, Scala, and Spark. Brush up on your knowledge of distributed systems and scalable algorithms, as these will likely be focal points during technical interviews. Be prepared to discuss your experience with large-scale data processing and how you've implemented machine learning algorithms in past projects.

Prepare for a Multi-Stage Interview Process

Criteo's interview process typically involves multiple stages, including HR screening, technical assessments, and behavioral interviews. Each stage serves a different purpose, so tailor your preparation accordingly. For technical interviews, practice coding problems on platforms like LeetCode, focusing on medium to hard difficulty levels. For behavioral interviews, use the STAR method (Situation, Task, Action, Result) to articulate your experiences clearly and effectively.

Showcase Your Problem-Solving Skills

During the interviews, you may encounter case studies or design problems that require you to demonstrate your analytical thinking and problem-solving abilities. Approach these questions collaboratively, as if you are working with the interviewer to find a solution. Discuss your thought process openly, and don't hesitate to ask clarifying questions if needed. This will not only show your technical skills but also your ability to communicate and work as part of a team.

Emphasize Cultural Fit

Criteo values innovation, teamwork, and a vibrant culture. Be prepared to discuss how your personal values align with the company's core principles. Share examples of how you've contributed to team success in previous roles and how you embrace a collaborative work environment. Highlight your curiosity and drive to solve complex problems, as these traits resonate well with Criteo's mission.

Be Ready for Technical Challenges

Expect to face technical challenges that test your coding and algorithmic skills. Practice common data structures and algorithms, and be ready to explain your reasoning and approach to solving problems. Familiarize yourself with concepts like complexity analysis, as interviewers may ask you to evaluate the efficiency of your solutions.

Follow Up Professionally

After your interviews, consider sending a thank-you email to express your appreciation for the opportunity and reiterate your interest in the position. This not only demonstrates professionalism but also keeps you on the interviewers' radar. If you don't hear back within the expected timeframe, a polite follow-up can help you gain clarity on your application status.

By following these tips and preparing thoroughly, you'll position yourself as a strong candidate for the Machine Learning Engineer role at Criteo. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at Criteo. The interview process will likely assess your technical skills in machine learning, programming, and data analysis, as well as your problem-solving abilities and cultural fit within the company. Be prepared to demonstrate your knowledge of algorithms, data structures, and your experience with big data technologies.

Machine Learning

1. Which machine learning algorithms are you most familiar with, and can you explain how one of them works?

Criteo is interested in your understanding of machine learning algorithms and their applications.

How to Answer

Discuss a few algorithms you have experience with, focusing on one in detail. Explain its purpose, how it works, and any specific use cases you have encountered.

Example

“I am well-versed in algorithms such as logistic regression, decision trees, and random forests. For instance, logistic regression is used for binary classification problems. It estimates the probability that a given input belongs to a particular category by applying a logistic function to a linear combination of the input features.”

2. How do you handle overfitting in your models?

Understanding overfitting is crucial for building robust machine learning models.

How to Answer

Explain the concept of overfitting and discuss techniques you use to mitigate it, such as cross-validation, regularization, or pruning.

Example

“To handle overfitting, I often use techniques like cross-validation to ensure that my model generalizes well to unseen data. Additionally, I apply regularization methods like L1 or L2 regularization to penalize overly complex models, which helps in reducing overfitting.”

3. Can you describe a project where you implemented a machine learning model? What challenges did you face?

Criteo values practical experience and problem-solving skills.

How to Answer

Share a specific project, detailing the problem, your approach, and the outcome. Highlight any challenges and how you overcame them.

Example

“In a recent project, I developed a recommendation system using collaborative filtering. One challenge was dealing with sparse data, which I addressed by implementing matrix factorization techniques. This significantly improved the model's accuracy and user satisfaction.”

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

Performance evaluation is key to understanding model effectiveness.

How to Answer

Discuss various metrics you use to evaluate models, 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, while I prefer precision and recall for imbalanced datasets. For instance, in a fraud detection model, I focus on recall to ensure we catch as many fraudulent cases as possible, even at the cost of precision.”

Programming and Data Structures

1. What programming languages are you proficient in, and how have you used them in your projects?

Criteo seeks candidates with strong programming skills.

How to Answer

Mention the languages you are proficient in and provide examples of how you have used them in your work.

Example

“I am proficient in Python and Scala. In my previous role, I used Python for data preprocessing and model training, leveraging libraries like Pandas and Scikit-learn. I also utilized Scala for building scalable data processing pipelines on Spark.”

2. Can you explain the difference between a list and a tuple in Python?

This question tests your understanding of data structures.

How to Answer

Clearly explain the differences in terms of mutability, performance, and use cases.

Example

“A list in Python is mutable, meaning it can be modified after creation, while a tuple is immutable. This makes tuples faster and more memory-efficient, which is why I prefer using them for fixed collections of items, such as coordinates or configuration settings.”

3. How would you optimize a slow-running SQL query?

Criteo values efficiency in data handling.

How to Answer

Discuss strategies for query optimization, such as indexing, query restructuring, or using appropriate joins.

Example

“To optimize a slow-running SQL query, I would first analyze the execution plan to identify bottlenecks. I might add indexes to frequently queried columns, restructure the query to minimize joins, or use subqueries to reduce the dataset size before applying filters.”

Big Data Technologies

1. What experience do you have with Hadoop and MapReduce?

Criteo's infrastructure relies heavily on big data technologies.

How to Answer

Share your experience with Hadoop and MapReduce, including specific projects or tasks you have completed.

Example

“I have over three years of experience with Hadoop and MapReduce. In my last project, I used MapReduce to process large datasets for a customer segmentation analysis, which allowed us to derive insights from terabytes of data efficiently.”

2. How do you ensure data quality when working with large datasets?

Data quality is critical in machine learning and analytics.

How to Answer

Discuss methods you use to ensure data quality, such as validation checks, data cleaning, and monitoring.

Example

“I ensure data quality by implementing validation checks during data ingestion, performing regular data cleaning to handle missing or inconsistent values, and setting up monitoring systems to track data quality metrics over time.”

3. Can you describe a time when you had to troubleshoot a big data processing issue?

Criteo values problem-solving skills in a big data context.

How to Answer

Share a specific instance where you identified and resolved a big data issue, detailing your approach and the outcome.

Example

“Once, I encountered a performance issue with a MapReduce job that was taking too long to complete. I analyzed the job's execution logs and discovered that a particular reducer was overloaded. I optimized the data partitioning strategy, which significantly reduced the processing time and improved overall job performance.”