Kiewit Machine Learning Engineer Interview Questions + Guide in 2025

Kiewit is a leading construction and engineering firm known for its innovative approaches to infrastructure projects across North America.

As a Machine Learning Engineer at Kiewit, you will be responsible for designing, building, and deploying machine learning models that enhance project efficiency and decision-making processes within the construction industry. Key responsibilities include collaborating with cross-functional teams to identify opportunities for machine learning applications, developing algorithms and predictive models, and ensuring the scalability and reliability of these solutions in real-world environments. The ideal candidate will possess a strong foundation in programming languages such as Python or Java, a solid understanding of data structures and algorithms, and experience in data manipulation and statistical analysis. Furthermore, an ability to communicate complex technical concepts to non-technical stakeholders and a passion for leveraging technology to solve real-world problems will set you apart in this role.

This guide is designed to equip you with insights into the expectations and culture at Kiewit, helping you articulate your experiences and demonstrate your expertise effectively during the interview process.

The interview process for a Machine Learning Engineer at Kiewit is structured to assess both technical expertise and cultural fit within the company. The process typically unfolds in several key stages:

1. Initial Phone Screen

The first step is an initial phone screen, which usually lasts around 30 to 45 minutes. During this conversation, a recruiter will discuss your background, skills, and experiences relevant to the role. This is also an opportunity for you to learn more about Kiewit’s culture and the specifics of the Machine Learning Engineer position. Expect questions about your past projects, programming languages you are comfortable with, and your long-term career goals.

2. Technical Interview

Following the initial screen, candidates typically undergo a technical interview, which may be conducted via video call. This interview focuses on your technical knowledge and problem-solving abilities. You may be asked to solve coding challenges or answer questions related to machine learning concepts, programming paradigms, and software development practices. Be prepared to discuss specific projects you have worked on and the technologies you utilized.

3. Behavioral Interview

The behavioral interview is designed to assess how well you align with Kiewit’s values and work culture. This round may involve questions about your teamwork experiences, challenges you have faced, and how you handle feedback. Interviewers will be interested in understanding your interpersonal skills and how you approach collaboration in a team setting.

4. Final Interview

In some cases, a final interview may be conducted with hiring managers or senior team members. This round often delves deeper into your technical expertise and may include discussions about Kiewit’s future projects and expansion plans. Candidates should be ready to articulate their vision for their role within the company and how they can contribute to its growth.

As you prepare for your interview, consider the types of questions that may arise in each of these stages.

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

Understand the Company’s Growth and Culture

Kiewit is in a phase of expansion, and understanding their growth plans can give you an edge. Familiarize yourself with their recent projects and how they leverage machine learning in their operations. This knowledge will not only help you answer questions more effectively but also demonstrate your genuine interest in the company’s future. Be prepared to discuss how your skills can contribute to their growth and align with their strategic goals.

Prepare for Technical Questions

As a Machine Learning Engineer, you can expect a strong focus on your technical knowledge. Brush up on key concepts such as algorithms, data structures, and programming languages relevant to the role. Be ready to discuss your experience with machine learning frameworks and tools, as well as any projects you’ve worked on that showcase your skills. Practice articulating your thought process clearly, as interviewers may ask you to solve problems on the spot.

Be Ready for Behavioral Questions

Kiewit interviewers may ask behavioral questions to gauge your fit within their team and culture. Prepare to discuss your past experiences, particularly challenges you’ve faced and how you overcame them. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you highlight your problem-solving abilities and teamwork skills. This will help you convey your adaptability and resilience, which are crucial traits for a Machine Learning Engineer.

Stay Positive and Engaged

While some candidates have reported negative experiences with interviewers, it’s essential to maintain a positive demeanor throughout your interview. Engage with your interviewer by asking insightful questions about the team dynamics and company culture. This not only shows your interest but also helps you assess if Kiewit is the right fit for you. Remember, interviews are a two-way street, and your attitude can leave a lasting impression.

Tailor Your Responses to the Role

When discussing your background, focus on experiences that are directly relevant to the Machine Learning Engineer position. Highlight specific projects where you applied machine learning techniques, the challenges you faced, and the outcomes of your work. This targeted approach will help the interviewers see the direct correlation between your experience and the role you are applying for.

Practice Coding Challenges

Some candidates have mentioned coding challenges as part of the interview process. Make sure to practice coding problems that are relevant to machine learning and data analysis. Familiarize yourself with common algorithms and data manipulation techniques, as well as the programming languages you are most comfortable with. This preparation will help you feel more confident when tackling technical assessments during the interview.

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

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at Kiewit. The interview process will likely focus on your technical expertise, problem-solving abilities, and how your experiences align with the company's growth and technological advancements. Be prepared to discuss your projects, programming languages, and how you approach challenges in machine learning.

Technical Knowledge

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

Understanding the fundamental concepts of machine learning is crucial.

How to Answer

Clearly define both terms and provide examples of algorithms used in each category.

Example

“Supervised learning involves training a model on labeled data, where the outcome is known, such as classification tasks using algorithms like decision trees. In contrast, unsupervised learning deals with unlabeled data, aiming to find hidden patterns, such as clustering using K-means.”

2. What programming languages are you most comfortable with, and why?

This question assesses your technical proficiency and preferences.

How to Answer

Mention the languages you have experience with, highlighting any specific projects or applications where you utilized them.

Example

“I am most comfortable with Python due to its extensive libraries for machine learning, such as TensorFlow and scikit-learn. I also have experience with R for statistical analysis and data visualization.”

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

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

How to Answer

Discuss a specific project, the challenges encountered, and how you overcame them.

Example

“I worked on a predictive maintenance project for industrial equipment. One challenge was dealing with imbalanced datasets. I implemented techniques like SMOTE to generate synthetic samples, which improved the model's performance significantly.”

4. What is a virtual function, and how does it relate to inheritance in programming?

This question tests your understanding of object-oriented programming concepts.

How to Answer

Define virtual functions and explain their role in inheritance, providing a brief example if possible.

Example

“A virtual function allows derived classes to override it, enabling polymorphism. For instance, if we have a base class ‘Shape’ with a virtual function ‘area’, derived classes like ‘Circle’ and ‘Square’ can implement their own versions of ‘area’.”

5. How do you approach feature selection in a machine learning model?

This question assesses your understanding of model optimization.

How to Answer

Discuss the methods you use for feature selection and why they are important.

Example

“I use techniques like Recursive Feature Elimination (RFE) and feature importance from tree-based models to select relevant features. This helps in reducing overfitting and improving model interpretability.”

Behavioral Questions

1. Describe a time when you faced a significant challenge in a project. How did you handle it?

This question evaluates your resilience and problem-solving skills.

How to Answer

Provide a specific example, focusing on the actions you took and the outcome.

Example

“In a previous project, we faced a tight deadline due to unexpected data quality issues. I organized a team meeting to brainstorm solutions, and we implemented a data cleaning pipeline that allowed us to meet the deadline without compromising quality.”

2. What are your long-term career goals, and how does this position fit into them?

This question gauges your ambition and alignment with the company’s vision.

How to Answer

Discuss your career aspirations and how the role aligns with your goals.

Example

“My long-term goal is to lead a team of machine learning engineers. This position at Kiewit offers the opportunity to work on innovative projects and develop my leadership skills, which aligns perfectly with my career path.”

3. How do you stay updated with the latest trends in machine learning?

This question assesses your commitment to continuous learning.

How to Answer

Mention specific resources, communities, or courses you engage with to stay informed.

Example

“I regularly read research papers on arXiv, follow influential machine learning blogs, and participate in online courses on platforms like Coursera. I also attend local meetups to network with other professionals in the field.”

4. Can you describe a time when you had to work with a difficult team member?

This question evaluates your interpersonal skills and teamwork.

How to Answer

Share a specific experience, focusing on how you managed the situation.

Example

“I once worked with a team member who was resistant to feedback. I scheduled a one-on-one meeting to discuss our project goals and listened to their concerns. By fostering open communication, we were able to collaborate more effectively and complete the project successfully.”

5. What kind of role do you see yourself in within the next few years?

This question helps the interviewer understand your career trajectory.

How to Answer

Discuss your aspirations and how you envision your growth within the company.

Example

“In the next few years, I see myself taking on more leadership responsibilities, possibly as a senior machine learning engineer or team lead, where I can mentor others and drive innovative projects at Kiewit.”