Remitly Machine Learning Engineer Interview Questions + Guide in 2025

Remitly is a forward-thinking company dedicated to transforming lives with trusted financial services that transcend borders, aiming to provide customers with access to essential services no matter where they are.

As a Machine Learning Engineer at Remitly, you will be at the forefront of developing models that assess the riskiness of customers and transactions, directly impacting the company’s fraud prevention efforts. Your key responsibilities will include building machine learning models capable of making automated decisions on international money transfers, detecting malicious activities, and collaborating with engineers to ensure that your models meet necessary latency and uptime requirements. You’ll work closely with Data Scientists to analyze fraud trends and implement innovative modeling strategies to stay ahead of potential threats. A strong foundation in Python and ML libraries (such as scikit-learn, pandas, and Spark) is essential, along with hands-on experience in building production-level Machine Learning systems, particularly in classification problems.

The ideal candidate will possess a Bachelor’s degree in a quantitative field and have at least three years of relevant experience. They should be motivated by the opportunity to create a meaningful impact and capable of making tough technical trade-offs while balancing short-term outcomes with long-term effects. At Remitly, your work will not only contribute to the company's success but also have a direct positive effect on millions of customers around the world.

This guide will prepare you for your interview by clarifying the expectations and responsibilities of the Machine Learning Engineer role at Remitly, as well as equipping you with the insights needed to excel in your discussions.

The interview process for a Machine Learning Engineer at Remitly 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 alignment with the company's values.

1. Initial Screening

The process begins with a phone interview conducted by a recruiter. This initial screening lasts about 30 minutes and focuses on understanding your background, motivations for applying to Remitly, and basic alignment with the company's values. Expect to discuss your previous experiences and how they relate to the role.

2. Technical Assessment

Following the initial screening, candidates are often required to complete a technical assessment. This may involve a coding challenge or a take-home project that tests your proficiency in relevant programming languages and machine learning concepts. The assessment is designed to evaluate your problem-solving skills and your ability to apply machine learning algorithms in practical scenarios.

3. Interview with Hiring Manager

After successfully completing the technical assessment, candidates typically have a one-on-one interview with the hiring manager. This interview delves deeper into your technical expertise, particularly in building production machine learning systems. Expect questions about your experience with machine learning algorithms, Python, SQL, and any relevant libraries. Behavioral questions may also be included to gauge your fit within the team and company culture.

4. Panel Interview

The final stage usually consists of a panel interview, which may include multiple team members from different functions such as engineering, data science, and product management. This round is more comprehensive, often lasting several hours and covering a mix of technical, behavioral, and case study questions. The panel will assess your ability to collaborate across teams and your understanding of the company's mission and values.

Throughout the interview process, candidates are encouraged to ask questions to demonstrate their interest in the role and the company. This is a great opportunity to showcase your understanding of Remitly's goals and how you can contribute to their mission.

Next, let's explore 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's Impact

As a Machine Learning Engineer at Remitly, your work directly influences fraud prevention and customer trust. Familiarize yourself with the specific challenges in fraud detection and how machine learning can address them. Be prepared to discuss how your previous experiences can contribute to building models that assess risk and detect malicious activities. Highlight any relevant projects that demonstrate your ability to create impactful solutions.

Prepare for Technical Assessments

Expect a mix of coding challenges and case studies during the interview process. Brush up on your Python skills and familiarize yourself with libraries like scikit-learn, pandas, and Spark. Practice solving classification problems and be ready to explain your thought process. Given the emphasis on practical applications, consider working on projects that involve real-world data to showcase your ability to build production-level machine learning systems.

Emphasize Collaboration

Collaboration is key at Remitly, as you will be working closely with engineers and data scientists. Be prepared to discuss how you have successfully collaborated in past roles, particularly in cross-functional teams. Share examples of how you’ve communicated complex technical concepts to non-technical stakeholders, as this will demonstrate your ability to bridge gaps between teams.

Align with Company Values

Remitly places a strong emphasis on its values, such as customer centricity and constructive directness. Prepare to share specific examples from your past experiences that align with these values. For instance, discuss a time when you prioritized customer needs in your work or how you handled a disagreement constructively. This will show that you not only have the technical skills but also fit well within the company culture.

Be Ready for Behavioral Questions

Expect a significant portion of the interview to focus on behavioral questions. Prepare for questions that start with "Tell me about a time when..." and have specific examples ready that highlight your problem-solving skills, adaptability, and decision-making processes. Use the STAR (Situation, Task, Action, Result) method to structure your responses effectively.

Ask Thoughtful Questions

At the end of each interview, you will likely have the opportunity to ask questions. Use this time to demonstrate your interest in the role and the company. Inquire about the team dynamics, the challenges they face in fraud prevention, or how success is measured for the Machine Learning Engineer role. This not only shows your enthusiasm but also helps you gauge if the company is the right fit for you.

Follow Up Professionally

After your interviews, send a thank-you email to express your appreciation for the opportunity to interview. Reiterate your interest in the role and briefly mention a key point from your conversation that resonated with you. This will help keep you top of mind and demonstrate your professionalism.

By following these tips, you can present yourself as a well-rounded candidate who is not only technically proficient but also aligned with Remitly's mission and values. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at Remitly. The interview process will likely focus on your technical skills in machine learning, your understanding of algorithms, and your ability to work collaboratively with cross-functional teams. Be prepared to discuss your past experiences, particularly those that demonstrate your problem-solving abilities and your alignment with the company's values.

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, emphasizing how supervised learning uses labeled data while unsupervised learning deals with unlabeled data. 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 using logistic regression for classification tasks. In contrast, unsupervised learning, like k-means clustering, analyzes data without predefined labels to find hidden patterns.”

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

This question assesses your practical experience and problem-solving skills.

How to Answer

Outline the project scope, your role, the challenges encountered, and how you overcame them. Highlight any specific algorithms or tools you used.

Example

“I worked on a fraud detection system where we faced challenges with imbalanced data. We implemented SMOTE to generate synthetic samples for the minority class, which improved our model's accuracy significantly.”

3. How do you handle overfitting in your models?

This question tests your understanding of model performance and generalization.

How to Answer

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

Example

“To combat overfitting, I use techniques like cross-validation to ensure the model performs well on unseen data. Additionally, I apply L1 and L2 regularization to penalize overly complex models.”

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 classification and regression tasks, and explain when to use each.

Example

“I typically use accuracy, precision, recall, and F1-score for classification tasks, while RMSE and R-squared are my go-to metrics for regression. The choice depends on the specific problem and the importance of false positives versus false negatives.”

5. Can you explain how a decision tree works?

This question assesses your understanding of specific algorithms.

How to Answer

Describe the structure of a decision tree and how it makes decisions based on feature values.

Example

“A decision tree splits the data into subsets based on feature values, creating branches until it reaches a leaf node that represents a class label. The splits are determined by criteria like Gini impurity or information gain.”

Statistics & Probability

1. What is the Central Limit Theorem and why is it important?

This question tests your foundational knowledge in statistics.

How to Answer

Explain the theorem and its implications for sampling distributions.

Example

“The Central Limit Theorem states that the distribution of the sample means approaches a normal distribution as the sample size increases, regardless of the population's distribution. This is crucial for making inferences about population parameters.”

2. How do you handle missing data in a dataset?

This question evaluates your data preprocessing skills.

How to Answer

Discuss various strategies for dealing with missing data, such as imputation or removal.

Example

“I handle missing data by first analyzing the extent and pattern of the missingness. Depending on the situation, I might use mean imputation for numerical data or drop rows with excessive missing values to maintain data integrity.”

3. Explain the concept of p-value in hypothesis testing.

This question assesses your understanding of statistical significance.

How to Answer

Define p-value and its role in hypothesis testing.

Example

“A p-value indicates the probability of observing the data, or something more extreme, assuming the null hypothesis is true. A low p-value suggests that we can reject the null hypothesis, indicating statistical significance.”

4. What is the difference between Type I and Type II errors?

This question tests your knowledge of hypothesis testing errors.

How to Answer

Clearly define both types of errors and their implications.

Example

“A Type I error occurs when we reject a true null hypothesis, while a Type II error happens when we fail to reject a false null hypothesis. Understanding these errors is crucial for interpreting the results of hypothesis tests.”

5. How do you determine if a dataset is normally distributed?

This question evaluates your statistical analysis skills.

How to Answer

Discuss methods such as visual inspection, statistical tests, and skewness/kurtosis.

Example

“I assess normality using visual methods like Q-Q plots and histograms, along with statistical tests like the Shapiro-Wilk test. If the p-value from the test is below a certain threshold, I conclude that the data is not normally distributed.”

SQL and Data Manipulation

1. How do you optimize a SQL query?

This question tests your SQL skills and understanding of performance.

How to Answer

Discuss indexing, query structure, and execution plans.

Example

“I optimize SQL queries by using indexes on frequently queried columns, avoiding SELECT *, and analyzing execution plans to identify bottlenecks. This approach significantly reduces query execution time.”

2. Can you explain the difference between INNER JOIN and LEFT JOIN?

This question assesses your knowledge of SQL joins.

How to Answer

Define both types of joins and provide examples of when to use each.

Example

“An INNER JOIN returns only the rows with matching values in both tables, while a LEFT JOIN returns all rows from the left table and matched rows from the right table, filling in NULLs where there are no matches. I use INNER JOIN when I need only the intersecting data, and LEFT JOIN when I want to retain all records from the left table.”

3. Write a SQL query to find the second highest salary from a table.

This question tests your practical SQL skills.

How to Answer

Provide a clear SQL query and explain your logic.

Example

“SELECT MAX(salary) FROM employees WHERE salary < (SELECT MAX(salary) FROM employees); This query finds the maximum salary that is less than the highest salary, effectively giving the second highest salary.”

4. How do you handle large datasets in SQL?

This question evaluates your data management skills.

How to Answer

Discuss techniques such as partitioning, indexing, and using aggregate functions.

Example

“I handle large datasets by partitioning tables to improve query performance, using indexes to speed up searches, and applying aggregate functions to summarize data efficiently.”

5. Explain the concept of normalization in databases.

This question tests your understanding of database design.

How to Answer

Define normalization and its purpose in database design.

Example

“Normalization is the process of organizing data in a database to reduce redundancy and improve data integrity. It involves dividing large tables into smaller, related tables and defining relationships between them.”