Agoda Machine Learning Engineer Interview Questions + Guide in 2025

Agoda is a leading online travel booking platform that connects travelers with millions of accommodations across the globe, leveraging cutting-edge technology to enhance user experiences.

As a Machine Learning Engineer at Agoda, you will play a pivotal role in developing and optimizing machine learning systems that significantly impact the company's operations. Key responsibilities include leading technical improvements to existing ML systems, designing and building ML processing pipelines, and deploying new libraries and frameworks while maintaining high standards of quality. A strong understanding of various machine learning models, such as Random Forest and Convolutional Neural Networks, is essential, as well as proficiency in programming languages like Python and tools like Pyspark. You will also need to navigate large-scale data architecture and ensure systems are robust and scalable.

To excel in this role, you should embody Agoda's values of innovation and collaboration, demonstrating a passion for problem-solving and continuous improvement while effectively communicating with cross-functional teams. This guide will equip you with the insights and knowledge to confidently approach your interview, showcasing your technical expertise and alignment with Agoda's mission.

The interview process for a Machine Learning Engineer at Agoda is structured to assess both technical skills and cultural fit within the team. It typically consists of several stages, each designed to evaluate different competencies relevant to the role.

1. Initial Screening

The process begins with an initial screening, which is often a phone interview with a recruiter. This conversation focuses on your background, experience, and motivation for applying to Agoda. The recruiter will also assess your fit for the company culture and may ask about your understanding of machine learning concepts and technologies.

2. Technical Assessment

Following the initial screening, candidates are usually required to complete a technical assessment. This may include an online coding test that evaluates your proficiency in algorithms, data structures, and SQL. The assessment typically consists of multiple-choice questions and coding challenges that you can complete within a specified time frame. Expect questions that require you to demonstrate your understanding of machine learning algorithms, as well as practical coding skills in Python or other relevant languages.

3. Technical Interviews

Candidates who pass the technical assessment will move on to one or more technical interviews. These interviews are conducted by team members, including data scientists and engineers. During these sessions, you will be asked to solve coding problems in real-time, discuss your previous projects, and explain your approach to machine learning challenges. Be prepared to answer questions about specific algorithms, model evaluation metrics, and your experience with tools like Pyspark and Hadoop.

4. Case Study or Take-Home Assignment

In some instances, candidates may be given a case study or a take-home assignment that requires them to apply their machine learning knowledge to a practical problem. This could involve building a model, analyzing data, or designing a solution to a specific business challenge. The goal is to assess your problem-solving skills and your ability to apply theoretical knowledge in a real-world context.

5. Final Interview

The final stage of the interview process often includes a cultural fit interview, which may involve discussions with senior team members or stakeholders. This interview focuses on your alignment with Agoda's values and your ability to work collaboratively within a diverse team. You may also be asked about your long-term career goals and how you envision contributing to Agoda's mission.

As you prepare for your interview, it's essential to familiarize yourself with the types of questions that may be asked during each stage of the process.

In this section, we’ll review the various interview questions that might be asked during an interview for a Machine Learning Engineer position at Agoda. The interview process will likely focus on your technical expertise in machine learning algorithms, programming skills, and your ability to solve complex problems. Be prepared to discuss your past experiences and demonstrate your knowledge through coding exercises and theoretical questions.

Machine Learning Algorithms

1. Explain how a decision tree is used to classify data.

Understanding decision trees is fundamental in machine learning, and you should be able to articulate their structure and function.

How to Answer

Discuss the concept of decision nodes, leaf nodes, and how the tree splits based on feature values to make predictions.

Example

“A decision tree classifies data by splitting it into branches based on feature values. Each internal node represents a decision based on a feature, and each leaf node represents a class label. The tree is built by selecting the feature that provides the best split at each node, often using metrics like Gini impurity or information gain.”

2. What are the advantages and disadvantages of using Random Forest over a single decision tree?

This question tests your understanding of ensemble methods and their practical implications.

How to Answer

Highlight the benefits of reduced overfitting and improved accuracy, while also mentioning the increased complexity and computational cost.

Example

“Random Forest mitigates overfitting by averaging the predictions of multiple decision trees, which leads to better generalization on unseen data. However, it can be computationally intensive and less interpretable than a single decision tree.”

3. Describe the process of training a neural network.

This question assesses your knowledge of deep learning fundamentals.

How to Answer

Explain the steps involved, including data preparation, forward propagation, loss calculation, backpropagation, and optimization.

Example

“Training a neural network involves feeding input data through the network to obtain predictions, calculating the loss using a loss function, and then adjusting the weights through backpropagation to minimize this loss. This process is repeated iteratively until the model converges.”

4. How do you handle overfitting in machine learning models?

This question evaluates your understanding of model evaluation and tuning.

How to Answer

Discuss techniques such as cross-validation, regularization, and pruning.

Example

“To combat overfitting, I use techniques like cross-validation to ensure the model generalizes well to unseen data. Additionally, I apply regularization methods such as L1 or L2 regularization to penalize overly complex models and consider pruning methods for decision trees.”

5. Can you explain the concept of gradient descent?

This question tests your understanding of optimization techniques used in machine learning.

How to Answer

Describe how gradient descent works and its variants.

Example

“Gradient descent is an optimization algorithm used to minimize the loss function by iteratively adjusting the model parameters in the opposite direction of the gradient. Variants like stochastic gradient descent and mini-batch gradient descent help improve convergence speed and efficiency.”

Programming and Algorithms

1. Write a program to find the largest possible earnings given a time series of stock prices.

This coding question assesses your algorithmic thinking and coding skills.

How to Answer

Outline your approach to solving the problem, including any algorithms or data structures you would use.

Example

“I would iterate through the stock prices, keeping track of the minimum price seen so far and calculating the potential profit at each step. The maximum profit would be updated whenever a higher profit is found.”

2. How would you implement a binary search algorithm?

This question evaluates your understanding of search algorithms.

How to Answer

Explain the binary search process and its time complexity.

Example

“Binary search works by repeatedly dividing the search interval in half. If the target value is less than the middle element, the search continues in the lower half; otherwise, it continues in the upper half. This algorithm has a time complexity of O(log n).”

3. Explain the difference between depth-first search (DFS) and breadth-first search (BFS).

This question tests your knowledge of graph traversal algorithms.

How to Answer

Discuss the key differences in approach and use cases for each algorithm.

Example

“DFS explores as far as possible along each branch before backtracking, making it suitable for problems like pathfinding in mazes. In contrast, BFS explores all neighbors at the present depth prior to moving on to nodes at the next depth level, which is useful for finding the shortest path in unweighted graphs.”

4. What is the time complexity of quicksort, and how does it work?

This question assesses your understanding of sorting algorithms.

How to Answer

Explain the quicksort algorithm and its average and worst-case time complexities.

Example

“Quicksort is a divide-and-conquer algorithm that selects a pivot element and partitions the array into elements less than and greater than the pivot. Its average time complexity is O(n log n), but in the worst case, it can degrade to O(n^2) if the pivot is poorly chosen.”

5. Describe how you would optimize a SQL query.

This question evaluates your SQL skills and understanding of database performance.

How to Answer

Discuss indexing, query structure, and the use of EXPLAIN plans.

Example

“To optimize a SQL query, I would first analyze the query execution plan using the EXPLAIN statement to identify bottlenecks. I would then consider adding indexes on frequently queried columns, rewriting the query for efficiency, and ensuring that joins are performed on indexed fields.”