Sendbird Machine Learning Engineer Interview Questions + Guide in 2025

Sendbird is a leading provider of chat APIs, enabling mobile apps and websites to integrate real-time messaging seamlessly.

As a Machine Learning Engineer at Sendbird, you will play a pivotal role in enhancing the company’s capability to leverage data and machine learning for building advanced conversational features. Your primary responsibilities will include designing and developing analytics and machine learning products, creating scalable ETL data pipelines, and processing large volumes of data from diverse sources to extract actionable insights. Proficiency in programming languages such as Python, Java, or Scala is essential, along with a strong analytical mindset to work with unstructured datasets. A successful candidate will not only be technically adept but also exhibit a passion for creating real-time chat solutions that enhance user experience.

In this role, you will collaborate cross-functionally with various teams to drive data-related product initiatives, ensuring that Sendbird continues to lead in the integration of real-time conversational technologies. Your ability to navigate the complexities of data processing, coupled with an understanding of machine learning methodologies, will position you as a key contributor to the company’s mission.

This guide will help you prepare for your interview by providing insights into the skills and experiences that are critical for success in this role, enabling you to present yourself as a well-rounded candidate who aligns with Sendbird's innovative culture.

The interview process for a Machine Learning Engineer at Sendbird is structured to assess both technical expertise and cultural fit within the team. The process typically unfolds as follows:

1. Initial Recruiter Screen

The first step is a phone screening with a recruiter, which usually lasts about 30 minutes. During this conversation, the recruiter will discuss the role, the company culture, and your background. This is an opportunity for you to express your interest in the position and ask any preliminary questions you may have about the company or the role.

2. Technical Assessment

Following the recruiter screen, candidates typically undergo a technical assessment. This may involve a coding challenge or a take-home assignment that focuses on your proficiency in Python, algorithms, and machine learning concepts. You will be given a week to complete this assessment, allowing you to demonstrate your problem-solving skills and technical knowledge.

3. Technical Interviews

After successfully completing the technical assessment, candidates will participate in one or more technical interviews. These interviews are conducted via video calls and usually last about an hour each. You can expect to tackle coding problems, discuss your approach to building ETL pipelines, and answer questions related to machine learning and data processing. Interviewers may also assess your understanding of distributed systems and your ability to work with unstructured datasets.

4. Team and Manager Interviews

Candidates will then meet with team members and the hiring manager. These interviews focus on your collaborative skills, your ability to work cross-functionally, and how you align with Sendbird's values. Expect questions that explore your past experiences, your approach to problem-solving, and how you handle conflicts within a team.

5. Cultural Fit Interview

The final step in the interview process is a cultural fit interview. This is designed to assess how well you embody the values of Sendbird and how you would contribute to the company culture. Be prepared to discuss how your personal values align with those of the organization and provide examples of how you have demonstrated these values in your previous roles.

As you prepare for your interviews, consider the specific skills and experiences that will be relevant to the questions you may encounter.

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

Understand the Company’s Vision

Before your interview, take the time to familiarize yourself with Sendbird's mission and the specific challenges they face in the realm of real-time conversational experiences. Understanding how your role as a Machine Learning Engineer fits into their broader goals will allow you to articulate how your skills can contribute to their success. Be prepared to discuss how your experience aligns with their vision of leveraging machine learning to enhance their products.

Prepare for Technical Proficiency

Given the emphasis on algorithms and Python in this role, ensure you are well-versed in these areas. Brush up on your knowledge of machine learning concepts, particularly those relevant to real-time data processing and analytics. Be ready to discuss your experience with building ETL pipelines and how you have utilized Python, Java, or Scala in past projects. Practicing coding problems and algorithms will also be beneficial, as technical interviews often include these components.

Showcase Your Problem-Solving Skills

During the interview, you may be presented with case studies or hypothetical scenarios. Approach these with a structured problem-solving mindset. Clearly outline your thought process, and don’t hesitate to ask clarifying questions if needed. Demonstrating your ability to break down complex problems and arrive at effective solutions will resonate well with the interviewers.

Emphasize Collaboration and Communication

Sendbird values cross-functional collaboration, so be prepared to discuss your experience working with diverse teams. Highlight instances where you successfully collaborated with product managers, data engineers, or other stakeholders to achieve a common goal. Your ability to communicate complex technical concepts to non-technical team members will be a significant asset.

Reflect on Company Culture

Interviews at Sendbird may include a culture fit component, so be ready to discuss how your values align with theirs. Familiarize yourself with their core values and think of examples from your past experiences that demonstrate how you embody these principles. This will not only show that you are a good fit for the team but also that you are genuinely interested in contributing to the company culture.

Be Ready for a Dynamic Interview Process

The interview process at Sendbird can vary, so be prepared for different formats, including technical assessments, panel interviews, and discussions with various team members. Stay adaptable and maintain a positive attitude throughout the process. If you encounter unexpected questions or scenarios, view them as opportunities to showcase your flexibility and critical thinking skills.

Follow Up Thoughtfully

After your interviews, consider sending a thoughtful follow-up email to express your gratitude for the opportunity and to reiterate your enthusiasm for the role. This not only demonstrates professionalism but also keeps you top of mind as they make their hiring decisions.

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

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at Sendbird. The interview process will likely focus on your technical skills, problem-solving abilities, and cultural fit within the team. Be prepared to discuss your experience with machine learning, data pipelines, and your approach to collaboration and conflict resolution.

Machine Learning

1. Can you describe a machine learning project you have worked on and the impact it had?

This question aims to assess your practical experience and understanding of machine learning applications.

How to Answer

Discuss the project’s objectives, the machine learning techniques you employed, and the results achieved. Highlight any challenges faced and how you overcame them.

Example

“I worked on a customer segmentation project where we used clustering algorithms to identify distinct user groups. This helped the marketing team tailor their campaigns, resulting in a 20% increase in engagement rates. The project involved preprocessing data, selecting features, and iterating on model performance to ensure accuracy.”

2. How do you handle overfitting in your models?

This question tests your understanding of model evaluation and optimization.

How to Answer

Explain the techniques you use to prevent overfitting, such as cross-validation, regularization, or pruning. Provide examples of when you applied these techniques.

Example

“To combat overfitting, I typically use cross-validation to ensure that my model generalizes well to unseen data. In a recent project, I implemented L1 and L2 regularization, which helped reduce the model complexity and improved its performance on the validation set.”

3. What metrics do you consider when evaluating a machine learning model?

This question assesses your knowledge of model performance evaluation.

How to Answer

Discuss various metrics relevant to the type of model you are working with, such as accuracy, precision, recall, F1 score, or AUC-ROC. Tailor your response to the context of the project.

Example

“I consider multiple metrics depending on the problem at hand. For classification tasks, I focus on precision and recall to understand the trade-offs between false positives and false negatives. In a recent binary classification project, I used the F1 score to balance these metrics effectively.”

4. How do you approach feature selection for your models?

This question evaluates your understanding of data preprocessing and feature engineering.

How to Answer

Explain your process for selecting relevant features, including techniques like correlation analysis, recursive feature elimination, or using domain knowledge.

Example

“I start with exploratory data analysis to identify potential features and their relationships with the target variable. I then use techniques like recursive feature elimination to systematically remove less important features, which helped improve model performance in a recent project.”

Data Engineering

1. Can you explain how you would design a data pipeline for a machine learning application?

This question assesses your understanding of data architecture and ETL processes.

How to Answer

Outline the steps involved in building a data pipeline, including data ingestion, processing, storage, and retrieval. Mention any tools or technologies you would use.

Example

“I would design a data pipeline that starts with data ingestion from various sources using Apache Kafka for real-time streaming. Then, I would process the data using Apache Spark for transformation and cleaning before storing it in a distributed database like Amazon Redshift for analysis and model training.”

2. What experience do you have with distributed data processing frameworks?

This question evaluates your familiarity with tools that handle large datasets.

How to Answer

Discuss your experience with frameworks like Apache Spark or Hadoop, including specific projects where you utilized them.

Example

“I have extensive experience with Apache Spark, particularly in processing large datasets for machine learning tasks. In one project, I used Spark’s MLlib to build a recommendation system, which allowed us to process terabytes of data efficiently and deliver real-time recommendations.”

3. How do you ensure data quality in your pipelines?

This question tests your understanding of data integrity and validation.

How to Answer

Explain the methods you use to validate and clean data, such as automated checks, data profiling, or anomaly detection.

Example

“I implement automated data validation checks at various stages of the pipeline to ensure data quality. For instance, I use schema validation to catch discrepancies early and employ anomaly detection algorithms to identify outliers in the data before it reaches the model.”

4. Describe your experience with cloud-based data solutions.

This question assesses your familiarity with cloud technologies relevant to data storage and processing.

How to Answer

Discuss your experience with cloud platforms like AWS, Azure, or Google Cloud, focusing on specific services you have used.

Example

“I have worked extensively with AWS, utilizing services like S3 for data storage and EMR for processing large datasets. In a recent project, I set up a data lake on S3, which allowed for scalable storage and easy access for our analytics team.”

Collaboration and Problem-Solving

1. How do you resolve conflicts among project team members?

This question evaluates your interpersonal skills and ability to work in a team.

How to Answer

Discuss your approach to conflict resolution, emphasizing communication and collaboration.

Example

“When conflicts arise, I prioritize open communication. I encourage team members to express their concerns and facilitate a discussion to find common ground. In a recent project, I mediated a disagreement over resource allocation, which led to a more collaborative approach and ultimately improved our project outcomes.”

2. Can you describe a time when you had to work cross-functionally?

This question assesses your ability to collaborate with different teams.

How to Answer

Provide an example of a project where you worked with other departments, highlighting the importance of collaboration.

Example

“I collaborated with the product and marketing teams to develop a machine learning feature that personalized user experiences. By aligning our goals and sharing insights, we successfully launched the feature, which increased user engagement by 30%.”

3. How do you prioritize tasks when working on multiple projects?

This question evaluates your time management and organizational skills.

How to Answer

Discuss your approach to prioritization, including any frameworks or tools you use.

Example

“I use a combination of the Eisenhower Matrix and project management tools like Trello to prioritize tasks based on urgency and importance. This approach helps me stay focused on high-impact activities while ensuring that I meet deadlines across multiple projects.”

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

This question assesses your commitment to continuous learning.

How to Answer

Share the resources you use to keep your knowledge current, such as online courses, conferences, or industry publications.

Example

“I regularly follow industry blogs, attend webinars, and participate in online courses to stay updated on the latest trends in machine learning and data engineering. I also engage with the community on platforms like GitHub and Stack Overflow to learn from others’ experiences.”