Quest Global Machine Learning Engineer Interview Questions + Guide in 2025

Quest Global is a leading engineering services provider that specializes in delivering innovative solutions across various industries, including aerospace, automotive, and healthcare.

As a Machine Learning Engineer at Quest Global, you will play a pivotal role in developing end-to-end machine learning pipelines that encompass the entire ML lifecycle—from data ingestion and transformation to model training, deployment, and evaluation. You will collaborate closely with AI scientists to accelerate the productionization of ML algorithms, ensuring that they are effectively integrated into operational environments. An integral part of your responsibilities will include establishing CI/CD/CT pipelines for ML algorithms, deploying models as a service in both cloud and on-premise settings, and managing a team of DevOps/MLOps engineers.

To excel in this role, you should possess strong programming skills, particularly in Python, and have a solid understanding of machine learning frameworks and cloud technologies, especially Google Cloud Platform (GCP). Familiarity with orchestration tools like MLflow and Kubeflow, as well as DevOps practices, will be crucial. Quest Global values collaboration and innovation, so a proactive approach to learning and applying new tools and technologies, along with strong team-oriented skills, will set you apart as an ideal candidate.

This guide aims to equip you with the knowledge and insights needed to navigate the interview process effectively, helping you to demonstrate your expertise and fit for the Machine Learning Engineer role at Quest Global.

The interview process for a Machine Learning Engineer at Quest Global is structured to assess both technical expertise and cultural fit within the organization. It typically consists of several key stages:

1. Online Aptitude Test

The first step in the interview process is an online aptitude test designed to evaluate candidates' quantitative, logical, and verbal skills. This test serves as a preliminary filter to ensure that candidates possess the foundational skills necessary for the role.

2. Technical Interview

Following the aptitude test, candidates will participate in a technical interview. This interview focuses on domain knowledge and problem-solving abilities relevant to machine learning and MLOps. Expect detailed questions that may cover topics such as model training, data pipelines, and programming in languages like Python. Candidates may also be asked to explain their previous projects and how they relate to the role.

3. HR Interview

The final stage of the interview process is an HR interview, which assesses candidates' communication skills, attitude, and overall cultural fit within Quest Global. This interview is crucial for determining how well candidates align with the company's values and work environment.

As you prepare for your interview, it's essential to be ready for a variety of questions that will test your technical knowledge and interpersonal skills.

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

Master the Technical Foundations

As a Machine Learning Engineer, you will be expected to demonstrate a strong grasp of algorithms, model training, and serving. Brush up on your knowledge of TensorFlow, PyTorch, and MLOps frameworks. Be prepared to discuss your experience with data pipelines and orchestration tools like Kubeflow and Apache Airflow. Familiarize yourself with the intricacies of model monitoring and management, as these are critical components of the role. Practicing coding problems that involve these technologies will give you a significant edge.

Prepare for the Aptitude Test

Quest Global's interview process begins with an online aptitude test that assesses quantitative, logical, and verbal skills. To prepare, practice with sample aptitude tests and focus on improving your problem-solving speed and accuracy. This will not only help you clear the initial round but also build your confidence for the subsequent technical interviews.

Showcase Your Projects

During the technical interview, you may be asked to explain your previous projects in detail. Be ready to discuss the challenges you faced, the solutions you implemented, and the impact of your work. Highlight any experience you have with end-to-end ML pipelines, as well as your collaboration with AI scientists to productionize algorithms. This will demonstrate your practical knowledge and ability to work in a team-oriented environment.

Emphasize Communication Skills

The HR interview will focus on assessing your communication, attitude, and cultural fit within the company. Be prepared to articulate your thoughts clearly and concisely. Practice discussing your experiences in a way that reflects your problem-solving approach and teamwork capabilities. Quest Global values collaboration, so showcasing your ability to work well with others will be crucial.

Understand the Company Culture

Quest Global emphasizes innovation and collaboration in its work environment. Familiarize yourself with the company's values and recent projects. This knowledge will help you align your responses with what the company is looking for in a candidate. Demonstrating an understanding of their mission and how you can contribute will set you apart from other candidates.

Stay Updated on Industry Trends

As a Machine Learning Engineer, staying current with the latest trends and technologies in AI and machine learning is essential. Be prepared to discuss recent advancements in the field and how they could apply to Quest Global's projects. This will not only show your passion for the industry but also your commitment to continuous learning and improvement.

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

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at Quest Global. The interview process will assess your technical expertise in machine learning, programming, and operations, as well as your problem-solving abilities and cultural fit within the company. Be prepared to discuss your past projects and experiences in detail, as well as demonstrate your knowledge of relevant tools and technologies.

Machine Learning

1. Can you explain the end-to-end machine learning lifecycle?

Understanding the complete lifecycle of machine learning is crucial for this role, as it involves data ingestion, transformation, model training, deployment, and evaluation.

How to Answer

Discuss each stage of the lifecycle, emphasizing your experience and any specific tools you have used at each step.

Example

“The machine learning lifecycle begins with data ingestion, where I gather and preprocess data. I then move on to data transformation, applying techniques like normalization and feature engineering. After that, I train the model using frameworks like TensorFlow or PyTorch, followed by deployment using CI/CD pipelines. Finally, I monitor the model's performance and retrain it as necessary to ensure it remains effective.”

2. What experience do you have with model deployment and serving?

This question assesses your practical experience in deploying machine learning models in production environments.

How to Answer

Highlight specific tools and frameworks you have used for deployment, such as TensorFlow Serving or Seldon Core, and discuss any challenges you faced.

Example

“I have deployed models using TensorFlow Serving, which allowed me to serve multiple versions of a model simultaneously. I faced challenges with scaling the service, but by implementing Kubernetes, I was able to manage the load effectively and ensure high availability.”

3. Describe a project where you collaborated with data scientists to productionize a machine learning model.

Collaboration is key in this role, and the interviewer wants to know how you work with others.

How to Answer

Focus on your role in the project, the communication methods you used, and the outcome of the collaboration.

Example

“In a recent project, I worked closely with data scientists to productionize a recommendation system. I facilitated regular meetings to discuss model performance and deployment strategies. By integrating their feedback into the CI/CD pipeline, we successfully launched the model, which improved user engagement by 20%.”

4. How do you monitor and manage machine learning models in production?

This question evaluates your understanding of model performance tracking and management.

How to Answer

Discuss the tools you use for monitoring and any metrics you consider important for model evaluation.

Example

“I use MLflow for tracking model performance and logging metrics such as accuracy and latency. Additionally, I set up alerts for any significant drops in performance, allowing for quick intervention and retraining if necessary.”

Programming and Tools

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

This question assesses your technical skills and experience with relevant programming languages.

How to Answer

Mention the languages you are comfortable with, particularly Python, and provide examples of how you have used them in your work.

Example

“I am proficient in Python, which I have used extensively for data manipulation with Pandas and for building machine learning models using libraries like Scikit-learn and TensorFlow. In my last project, I wrote scripts to automate data preprocessing, which significantly reduced the time taken to prepare data for training.”

2. Can you explain your experience with containerization and orchestration tools?

Understanding containerization and orchestration is essential for deploying machine learning models effectively.

How to Answer

Discuss your experience with tools like Docker and Kubernetes, and how they have helped you in your projects.

Example

“I have used Docker to containerize applications, ensuring consistency across different environments. Additionally, I utilized Kubernetes for orchestration, which allowed me to manage multiple containers and scale applications seamlessly during peak loads.”

3. Describe your experience with CI/CD pipelines in the context of machine learning.

This question focuses on your understanding of continuous integration and deployment practices.

How to Answer

Explain the CI/CD tools you have used and how they apply to machine learning workflows.

Example

“I have implemented CI/CD pipelines using Jenkins and GitHub Actions to automate the deployment of machine learning models. This process included automated testing of the model’s performance and integration with monitoring tools to ensure that any issues were caught early in the deployment process.”

4. What orchestration tools have you used for managing data pipelines?

This question assesses your familiarity with tools that help manage and automate data workflows.

How to Answer

Mention specific tools you have experience with, such as Apache Airflow or Kubeflow, and describe how you have used them.

Example

“I have used Apache Airflow to orchestrate data pipelines, allowing me to schedule and monitor workflows effectively. This experience helped streamline the data ingestion and transformation processes, ensuring that our models had access to the most up-to-date data.”

Problem-Solving and Technical Knowledge

1. Describe a challenging technical problem you faced and how you solved it.

This question evaluates your problem-solving skills and technical knowledge.

How to Answer

Choose a specific problem, explain the context, and detail the steps you took to resolve it.

Example

“In one project, we faced issues with model overfitting. I addressed this by implementing regularization techniques and cross-validation. Additionally, I gathered more diverse training data, which ultimately improved the model's generalization and performance on unseen data.”

2. How do you stay updated with the latest trends and technologies in machine learning?

This question assesses your commitment to continuous learning and professional development.

How to Answer

Discuss the resources you use to stay informed, such as online courses, conferences, or research papers.

Example

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