Rakuten Machine Learning Engineer Interview Questions + Guide in 2025

Rakuten is a leading global technology company that empowers individuals and businesses through innovative digital services, transforming the way people shop, travel, and communicate.

As a Machine Learning Engineer at Rakuten, you will play a critical role in developing and optimizing machine learning models that enhance user experiences across various platforms. Key responsibilities include designing, implementing, and maintaining scalable machine learning algorithms, analyzing large datasets to derive actionable insights, and collaborating with cross-functional teams to integrate ML solutions into existing products. The ideal candidate should possess strong programming skills in Python or Java, a solid understanding of machine learning frameworks (such as TensorFlow or PyTorch), and experience with data preprocessing and feature engineering. A passion for continuous learning, a proactive approach to problem-solving, and an ability to communicate complex technical concepts clearly are essential traits for success within Rakuten's dynamic and fast-paced environment.

This guide aims to equip you with the knowledge and insights necessary to excel in your interview, helping you to demonstrate your technical expertise, align with the company’s values, and confidently address potential questions related to your experience and the role.

The interview process for a Machine Learning Engineer at Rakuten is structured to assess both technical skills and cultural fit within the organization. It typically consists of several stages, each designed to evaluate different aspects of a candidate's qualifications and compatibility with the company's values.

1. Initial Screening

The process begins with an initial screening, which is often conducted by a recruiter. This stage usually involves a brief phone call where the recruiter discusses the role, the company culture, and gathers information about your background, skills, and motivations for applying. This is also an opportunity for candidates to ask questions about the expectations for the role and the working environment at Rakuten.

2. Technical Assessment

Following the initial screening, candidates may be required to complete a technical assessment. This assessment often includes coding challenges that can be done on platforms like Codility or HackerRank, focusing on data structures, algorithms, and problem-solving skills. The coding questions are generally at an easy to medium difficulty level, allowing candidates to demonstrate their technical proficiency.

3. Technical Interviews

Candidates who perform well in the technical assessment will move on to one or more technical interviews. These interviews typically involve discussions with engineers or hiring managers and may include live coding exercises, system design questions, and inquiries about past projects and experiences. Interviewers will assess not only technical knowledge but also the candidate's approach to problem-solving and their ability to communicate complex ideas clearly.

4. Behavioral Interviews

In addition to technical evaluations, candidates will also participate in behavioral interviews. These interviews focus on assessing cultural fit and interpersonal skills. Interviewers may ask about past experiences, teamwork, conflict resolution, and how candidates align with Rakuten's values. It’s important to prepare for questions that explore your motivations for joining Rakuten and your understanding of the company's mission.

5. Final Interview

The final stage of the interview process may involve a wrap-up interview with higher-level executives or team leads. This interview often serves as a formality to confirm the candidate's fit within the team and the organization. Candidates may be asked to discuss their long-term career goals, how they envision contributing to Rakuten, and any final questions they have about the role or the company.

The entire interview process can take several weeks, and candidates are encouraged to follow up with their recruiters for updates. Now, let’s delve into the specific interview questions that candidates have encountered during this process.

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

Understand the Role Expectations

Before your interview, take the time to clarify the expectations for the Machine Learning Engineer role at Rakuten. Given the diverse nature of software engineering roles, expectations can vary significantly. Ask your interviewer about how the expectations for this role may evolve over time, such as what success looks like in the first 30 days versus 90 days. This will not only demonstrate your proactive approach but also help you align your skills and experiences with their needs.

Prepare for Technical Assessments

Expect a rigorous technical assessment process, including coding tests and problem-solving questions. Familiarize yourself with platforms like Codility or LeetCode, as many candidates have reported using these for initial coding challenges. Focus on easy to medium-level problems, particularly in data structures and algorithms, as these are commonly tested. Additionally, brush up on your knowledge of machine learning frameworks and libraries relevant to the role, as well as any specific technologies mentioned in the job description.

Emphasize Cultural Fit

Rakuten places a strong emphasis on cultural fit during the interview process. Be prepared to discuss your motivations for wanting to work at Rakuten and how your values align with the company's mission. Reflect on your past experiences and how they have shaped your approach to teamwork and collaboration. Expect questions about your understanding of diversity and how you contribute to a positive work environment.

Showcase Your Projects

During the interview, be ready to discuss your previous projects in detail. Highlight your contributions, the challenges you faced, and the outcomes of your work. This is an opportunity to demonstrate your technical skills and problem-solving abilities. Prepare to explain the methodologies you used, the technologies involved, and any innovative solutions you implemented.

Communicate Clearly and Confidently

Throughout the interview, maintain clear and confident communication. Interviewers appreciate candidates who can articulate their thought processes, especially during technical discussions. If you encounter a challenging question, take a moment to think it through and explain your reasoning. This not only shows your analytical skills but also your ability to handle pressure.

Follow Up Professionally

After your interview, consider sending a follow-up email to express your gratitude for the opportunity and reiterate your interest in the position. This can help keep you top of mind for the interviewers and demonstrates your professionalism. If you have any specific points you wish to clarify or expand upon, this is a good time to do so.

By preparing thoroughly and approaching the interview with confidence and clarity, you can position yourself as a strong candidate for the Machine Learning Engineer role at Rakuten. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at Rakuten. The interview process will likely assess your technical skills in machine learning, programming, and data analysis, as well as your understanding of the company's culture and values. Be prepared to discuss your past experiences, problem-solving approaches, and how you can contribute to Rakuten's goals.

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 characteristics of both supervised and unsupervised learning, including how they are used in real-world applications.

Example

“Supervised learning involves training a model on labeled data, where the outcome is known, such as predicting house prices based on features like size and location. In contrast, unsupervised learning deals with unlabeled data, where the model tries to find patterns or groupings, like clustering customers based on purchasing behavior.”

2. What techniques do you use for feature selection?

Feature selection is vital for improving model performance. Be prepared to discuss various methods and their applications.

How to Answer

Mention techniques such as recursive feature elimination, LASSO regression, or tree-based methods, and explain when you would use each.

Example

“I often use recursive feature elimination to systematically remove features and assess model performance. Additionally, I find LASSO regression useful for both feature selection and regularization, especially when dealing with high-dimensional datasets.”

3. How do you handle imbalanced datasets?

Imbalanced datasets can skew model performance. Discuss strategies to address this issue.

How to Answer

Talk about techniques like resampling, using different evaluation metrics, or employing algorithms designed for imbalanced data.

Example

“To handle imbalanced datasets, I often use techniques like SMOTE for oversampling the minority class or undersampling the majority class. I also focus on metrics like F1-score or AUC-ROC instead of accuracy to better evaluate model performance.”

4. Can you describe a machine learning project you worked on?

This question assesses your practical experience and ability to communicate complex ideas.

How to Answer

Outline the problem, your approach, the tools you used, and the outcome of the project.

Example

“I worked on a project to predict customer churn for an e-commerce platform. I used logistic regression and random forests, analyzing customer behavior data. The model improved retention strategies, leading to a 15% reduction in churn rates.”

Programming and Technical Skills

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

Your programming skills are essential for a Machine Learning Engineer role. Be specific about your experience.

How to Answer

List the languages you are comfortable with and provide examples of how you have applied them in your work.

Example

“I am proficient in Python and R, using Python for data manipulation with libraries like Pandas and NumPy, and R for statistical analysis. In my last project, I used Python to build a machine learning pipeline that automated data preprocessing and model training.”

2. Explain the concept of overfitting and how to prevent it.

Overfitting is a common issue in machine learning. Be prepared to discuss it and your strategies for mitigation.

How to Answer

Define overfitting and mention techniques to prevent it, such as cross-validation, regularization, or pruning.

Example

“Overfitting occurs when a model learns noise in the training data rather than the underlying pattern. To prevent it, I use techniques like cross-validation to ensure the model generalizes well, and I apply regularization methods like L1 or L2 to penalize overly complex models.”

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

Understanding model evaluation is critical. Discuss the metrics you use and why they are important.

How to Answer

Mention various evaluation metrics and when to use them based on the problem type.

Example

“I evaluate model performance using metrics like accuracy, precision, recall, and F1-score for classification tasks, and RMSE or MAE for regression. The choice of metric depends on the specific business problem and the consequences of false positives or negatives.”

4. Describe your experience with cloud platforms for machine learning.

Cloud platforms are increasingly used for machine learning projects. Discuss your familiarity with them.

How to Answer

Mention specific platforms you have used and how they facilitated your projects.

Example

“I have experience using AWS and Google Cloud for deploying machine learning models. I utilized AWS SageMaker for building and training models at scale, which significantly reduced the time to deployment and allowed for easy integration with other AWS services.”

Behavioral and Cultural Fit

1. Why do you want to work at Rakuten?

This question assesses your motivation and alignment with the company’s values.

How to Answer

Discuss what attracts you to Rakuten, such as its culture, mission, or innovative projects.

Example

“I admire Rakuten’s commitment to innovation and its focus on leveraging technology to enhance customer experiences. I am excited about the opportunity to contribute to a company that values creativity and collaboration.”

2. How do you approach teamwork and collaboration in projects?

Collaboration is key in a team environment. Share your approach to working with others.

How to Answer

Discuss your communication style, how you handle conflicts, and your experience working in diverse teams.

Example

“I believe in open communication and actively seek feedback from my team members. I enjoy collaborating with cross-functional teams and have found that diverse perspectives lead to more innovative solutions. When conflicts arise, I address them directly and focus on finding common ground.”

3. Can you describe a challenging situation you faced in a project and how you overcame it?

This question evaluates your problem-solving skills and resilience.

How to Answer

Outline the challenge, your thought process, and the steps you took to resolve it.

Example

“In a previous project, we faced significant data quality issues that threatened our timeline. I organized a team meeting to brainstorm solutions, and we implemented a data cleaning process that not only resolved the issues but also improved our overall data pipeline efficiency.”

4. How do you stay updated with the latest trends in machine learning?

Continuous learning is essential in this field. Discuss your methods for staying informed.

How to Answer

Mention resources you use, such as journals, online courses, or conferences.

Example

“I regularly read research papers from arXiv and follow industry leaders on platforms like LinkedIn and Twitter. I also participate in online courses and attend conferences to network with other professionals and learn about the latest advancements in machine learning.”