Viasat Inc. Machine Learning Engineer Interview Questions + Guide in 2025

Viasat Inc. is a global communications company with a mission to deliver connections that change the world, serving consumers, businesses, governments, and militaries for over 35 years.

As a Machine Learning Engineer at Viasat, you will play a crucial role in developing innovative, vertically integrated products and services to enhance productivity and streamline MLOps-related activities. Key responsibilities include designing and maintaining APIs for machine learning models, deploying production models, and monitoring their performance over time. You will collaborate closely with cross-functional teams, including data scientists, cloud engineers, and software engineers. A strong foundation in algorithms and experience with Python and machine learning are essential, alongside traits such as adaptability, teamwork, and a passion for automation. This role aligns with Viasat's commitment to fostering a culture of inquiry, exploration, and learning.

This guide will help you prepare effectively for your interview by focusing on the specific skills and experiences that Viasat values in a Machine Learning Engineer, ultimately giving you a competitive edge in the hiring process.

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

1. Initial Phone Screen

The process begins with a brief phone interview, usually lasting around 20 to 30 minutes, conducted by a recruiter. This initial screen focuses on your resume, discussing your background, relevant experiences, and motivations for applying to Viasat. Expect questions about your understanding of the company and the role, as well as your salary expectations.

2. Technical Interview

Following the initial screen, candidates typically participate in a technical interview, which may be conducted via video call. This interview usually lasts between 30 to 45 minutes and focuses on coding and problem-solving skills. You may be asked to solve coding problems in real-time, often related to algorithms and data structures, as well as questions about object-oriented programming concepts. Familiarity with Python and machine learning frameworks is essential, as interviewers will assess your ability to apply these skills in practical scenarios.

3. Behavioral Interview

After the technical assessment, candidates often undergo a behavioral interview. This round is designed to evaluate your soft skills, teamwork, and cultural fit within Viasat. Expect questions that explore your past experiences, how you handle challenges, and your approach to collaboration. Interviewers may ask you to provide examples of how you've worked in teams or resolved conflicts in previous projects.

4. Final Panel Interview

The final stage typically involves a panel interview with multiple team members, including engineers and managers. This round can be more comprehensive, lasting up to an hour or more. Panelists will delve deeper into your technical knowledge, asking questions about your projects, machine learning concepts, and your understanding of APIs and cloud computing environments. This is also an opportunity for you to ask questions about the team dynamics and the projects you would be working on.

Throughout the interview process, candidates are encouraged to demonstrate their passion for machine learning and their ability to adapt to new challenges.

Next, let's explore the specific interview questions that candidates have encountered during their interviews at Viasat.

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

Understand the Interview Structure

The interview process at Viasat typically involves multiple rounds, including a phone screen, technical interviews, and possibly a panel interview. Be prepared for a mix of behavioral and technical questions. Familiarize yourself with the common structure: initial screening with HR, followed by technical discussions focusing on your resume and projects, and concluding with a final round that may involve multiple interviewers. Knowing this will help you manage your time and energy effectively throughout the process.

Highlight Your Technical Skills

As a Machine Learning Engineer, your proficiency in algorithms, Python, and machine learning concepts will be crucial. Brush up on your understanding of data structures and algorithms, as many interviewers will ask you to solve coding problems on the spot. Practice coding challenges on platforms like LeetCode or HackerRank, focusing on medium-level problems that require a solid understanding of algorithms and data structures. Be ready to discuss your past projects in detail, especially those that involved machine learning or API development.

Prepare for Behavioral Questions

Viasat values a collaborative and inclusive culture, so expect behavioral questions that assess your teamwork and problem-solving skills. Prepare to discuss specific instances where you worked in a team, faced challenges, or had to adapt to new situations. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you convey your thought process and the impact of your actions.

Know Your Projects

Be ready to dive deep into the projects listed on your resume. Interviewers will likely ask you to explain your role, the technologies you used, and the challenges you faced. Highlight any experience you have with machine learning systems, API development, and cloud computing environments like AWS or GCP. This not only demonstrates your technical skills but also shows your ability to apply them in real-world scenarios.

Ask Insightful Questions

At the end of your interviews, you will likely have the opportunity to ask questions. Use this time to demonstrate your interest in Viasat and the role. Inquire about the team dynamics, the types of projects you might work on, or how the company fosters innovation and learning. This not only shows your enthusiasm but also helps you gauge if Viasat is the right fit for you.

Stay Positive and Professional

Throughout the interview process, maintain a positive attitude, even if you encounter challenging questions or situations. Viasat values candidates who can remain composed under pressure and demonstrate a willingness to learn. Remember to express gratitude for the opportunity to interview and follow up with a thank-you note after your interviews, reiterating your interest in the position.

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

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at Viasat Inc. The interview process will likely assess your technical skills in machine learning, programming, and algorithms, as well as your ability to work collaboratively in a team environment. Be prepared to discuss your past projects and experiences, as well as demonstrate your problem-solving abilities.

Machine Learning

1. Can you explain the difference between supervised and unsupervised learning?

Understanding the fundamental concepts of machine learning is crucial. Be clear and concise in your explanation, providing examples of each type of learning.

How to Answer

Discuss the definitions of both supervised and unsupervised learning, highlighting the key differences in their applications and outcomes.

Example

“Supervised learning involves training a model on labeled data, where the input-output pairs are 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 identify patterns or groupings, like clustering customers based on purchasing behavior.”

2. What are some common metrics used to evaluate machine learning models?

This question tests your knowledge of model evaluation, which is critical for ensuring the effectiveness of machine learning solutions.

How to Answer

Mention various metrics such as accuracy, precision, recall, F1 score, and ROC-AUC, and explain when to use each.

Example

“Common metrics include accuracy for overall correctness, precision for the quality of positive predictions, recall for the ability to find all relevant instances, and F1 score for a balance between precision and recall. ROC-AUC is useful for evaluating the trade-off between true positive and false positive rates.”

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

This question allows you to showcase your practical experience and problem-solving skills.

How to Answer

Detail the project, your role, the technologies used, and the specific challenges encountered, along with how you overcame them.

Example

“I worked on a project to predict customer churn using logistic regression. One challenge was dealing with imbalanced data, which I addressed by implementing SMOTE to generate synthetic samples for the minority class, improving the model's performance significantly.”

4. How do you handle overfitting in a machine learning model?

Understanding overfitting is essential for developing robust models.

How to Answer

Discuss techniques such as cross-validation, regularization, and pruning that can help mitigate overfitting.

Example

“To handle overfitting, I use techniques like cross-validation to ensure the model generalizes well to unseen data. Additionally, I apply regularization methods like L1 and L2 to penalize overly complex models, and I may also reduce the number of features through feature selection.”

Programming and Algorithms

1. What is your experience with Python for machine learning?

This question assesses your programming skills, particularly in Python, which is widely used in machine learning.

How to Answer

Discuss your familiarity with Python libraries such as NumPy, pandas, scikit-learn, and TensorFlow, and provide examples of how you have used them.

Example

“I have extensive experience using Python for machine learning, particularly with libraries like scikit-learn for model building and evaluation, and TensorFlow for deep learning projects. For instance, I used TensorFlow to develop a convolutional neural network for image classification, achieving a high accuracy rate.”

2. Can you explain the concept of a neural network?

This question tests your understanding of deep learning, a key area in machine learning.

How to Answer

Provide a clear explanation of neural networks, including their structure and how they function.

Example

“A neural network consists of layers of interconnected nodes, or neurons, where each connection has an associated weight. The network learns by adjusting these weights through backpropagation during training, allowing it to model complex relationships in data. Each layer transforms the input data, enabling the network to learn hierarchical features.”

3. What is the difference between a stack and a queue?

This question evaluates your understanding of data structures, which are fundamental in programming.

How to Answer

Clearly define both data structures and their use cases.

Example

“A stack is a Last In First Out (LIFO) structure, where the last element added is the first to be removed, like a stack of plates. A queue, on the other hand, is a First In First Out (FIFO) structure, where the first element added is the first to be removed, similar to a line of people waiting for service.”

4. How would you implement a binary search algorithm?

This question tests your algorithmic thinking and coding skills.

How to Answer

Explain the binary search algorithm and its efficiency, and describe how you would implement it in code.

Example

“Binary search works on sorted arrays 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). I would implement it using a recursive or iterative approach, depending on the requirements.”

Behavioral Questions

1. Describe a time when you had to work in a team to achieve a goal.

This question assesses your teamwork and collaboration skills.

How to Answer

Share a specific example, focusing on your role, the team's dynamics, and the outcome.

Example

“In a university project, I collaborated with a team of four to develop a predictive analytics tool. I took the lead in data preprocessing and model selection, while others focused on front-end development. Our combined efforts resulted in a successful presentation and a high grade.”

2. How do you prioritize your 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 tools or methods you use.

Example

“I prioritize tasks based on deadlines and project impact. I use tools like Trello to organize my tasks and set clear milestones. For instance, during a recent internship, I managed multiple projects by breaking them down into smaller tasks and focusing on high-impact items first.”

3. Tell me about a time you faced a significant challenge in a project. How did you overcome it?

This question allows you to demonstrate your problem-solving abilities.

How to Answer

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

Example

“During a project, we encountered unexpected data quality issues that affected our model's accuracy. I initiated a thorough data cleaning process, collaborating with the team to identify and rectify the issues. This proactive approach not only improved our model but also enhanced our understanding of data quality's importance.”

4. Why do you want to work at Viasat?

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

How to Answer

Express your interest in Viasat's mission and how your skills align with their goals.

Example

“I am drawn to Viasat's commitment to innovation and its mission to connect people globally. I believe my background in machine learning and passion for developing impactful solutions align perfectly with your team's objectives, and I am excited about the opportunity to contribute to such meaningful work.”