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

Cummins Inc. is a global leader in power solutions, delivering high-performance engines and technology to meet the needs of various industries.

As a Machine Learning Engineer at Cummins Inc., you will play a crucial role in developing and implementing machine learning models that enhance operational efficiency and drive innovation. Key responsibilities include designing algorithms to analyze large datasets, collaborating with cross-functional teams to identify opportunities for machine learning integration, and conducting experiments to optimize model performance. Successful candidates will have a strong foundation in algorithms and data structures, supplemented by proficiency in Python for model development. Knowledge of machine learning methodologies and statistical analysis is essential, alongside a demonstrable ability to communicate complex concepts clearly to both technical and non-technical stakeholders. Ideal candidates will also exhibit traits such as problem-solving capabilities, adaptability, and a passion for continuous learning, aligning closely with Cummins' commitment to excellence and innovation.

This guide is designed to help you prepare effectively for your interview by providing insights into key focus areas and expectations specific to the Machine Learning Engineer role at Cummins Inc.

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

1. Application and Initial Screening

Candidates begin by submitting their applications online. Following this, a recruiter conducts an initial phone screening, which usually lasts around 30 minutes. During this call, the recruiter will discuss the role, gauge the candidate's interest, and assess their alignment with Cummins' values and culture. Expect to answer general behavioral questions and provide an overview of your background and experiences.

2. Technical Assessment

After the initial screening, candidates may be required to complete a technical assessment. This could involve online coding challenges or tests focused on algorithms, data structures, and machine learning concepts. The goal is to evaluate the candidate's problem-solving abilities and technical knowledge relevant to the role.

3. Panel Interviews

Successful candidates will then participate in a series of panel interviews, typically consisting of three to four interviewers. These interviews are designed to delve deeper into both technical and behavioral aspects. Expect questions related to machine learning algorithms, statistical methods, and your past projects. Interviewers will also assess your ability to work collaboratively and communicate effectively within a team.

4. Final Interview

The final stage usually involves a face-to-face interview with a technical lead and possibly other team members. This round focuses on more complex technical questions and situational scenarios that require critical thinking and decision-making skills. Candidates may also be asked to present their previous work or discuss how they would approach specific machine learning challenges relevant to Cummins' operations.

5. HR Round and Offer

If you successfully navigate the previous stages, you will have a final HR round, which typically includes discussions about salary expectations, benefits, and company policies. Following this, candidates can expect to receive an offer if they are deemed a good fit for the role and the company.

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

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

Understand the Company’s Focus on Supply Chain

Cummins Inc. places a significant emphasis on supply chain and IT practices. Familiarize yourself with the company's operations in these areas, as interviewers may ask about your interest and outlook on supply chain management. Be prepared to discuss how your skills in machine learning can contribute to optimizing supply chain processes.

Prepare for Behavioral Questions

Expect a variety of behavioral questions that assess your problem-solving abilities and teamwork skills. Reflect on your past experiences and be ready to share specific examples that demonstrate your ability to work collaboratively, handle conflicts, and contribute to team success. Questions like "Where do you see yourself in five years?" are common, so have a thoughtful answer that aligns with Cummins' values and your career aspirations.

Brush Up on Technical Skills

Given the technical nature of the Machine Learning Engineer role, ensure you are well-versed in algorithms and data structures, as these are crucial for the position. Review key concepts in machine learning, including regression techniques and statistical analysis, as these topics frequently arise in interviews. Practice coding challenges that focus on these areas to demonstrate your technical proficiency.

Emphasize Process Improvement and Collaboration

Cummins values process improvements and cross-functional teamwork. Be prepared to discuss how you can enhance existing processes and collaborate effectively with diverse teams. Highlight any past experiences where you successfully implemented improvements or worked with cross-functional teams to achieve a common goal.

Engage with the Interviewers

The interview process at Cummins is described as friendly and welcoming. Take this opportunity to engage with your interviewers by asking insightful questions about their experiences and the company culture. This not only shows your interest in the role but also helps you gauge if Cummins is the right fit for you.

Follow Up Professionally

After your interview, consider sending a thank-you email to express your appreciation for the opportunity to interview. This is a chance to reiterate your interest in the position and briefly mention how your skills align with the company's goals. A thoughtful follow-up can leave a positive impression and keep you top of mind as they make their decision.

By preparing thoroughly and approaching the interview with confidence and curiosity, you can position yourself as a strong candidate for the Machine Learning Engineer role at Cummins Inc. Good luck!

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

Technical Skills

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

Understanding the fundamental concepts of machine learning is crucial for this role.

How to Answer

Clearly define both supervised and unsupervised learning, providing examples of each. Highlight the scenarios in which you would use one over the other.

Example

“Supervised learning involves training a model on labeled data, where the outcome is known, such as predicting house prices based on features like size and location. In contrast, unsupervised learning deals with unlabeled data, aiming to find hidden patterns, like clustering customers based on purchasing behavior.”

2. What algorithms would you use for a regression problem?

This question tests your knowledge of algorithms relevant to machine learning tasks.

How to Answer

Discuss various regression algorithms, such as linear regression, decision trees, and support vector regression, and explain when you would use each.

Example

“For a regression problem, I would consider using linear regression for its simplicity and interpretability. If the relationship is non-linear, I might opt for decision trees or random forests, as they can capture complex patterns without requiring extensive feature engineering.”

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

Overfitting is a common issue in machine learning, and your approach to it is critical.

How to Answer

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

Example

“To handle overfitting, I would use techniques like cross-validation to ensure the model generalizes well to unseen data. Additionally, I might apply regularization methods like Lasso or Ridge regression to penalize overly complex models.”

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

This question allows you to showcase your practical experience.

How to Answer

Discuss a specific project, the challenges encountered, and how you overcame them, emphasizing your problem-solving skills.

Example

“I worked on a project to predict equipment failures in a manufacturing setting. One challenge was dealing with imbalanced data. I addressed this by using techniques like SMOTE to generate synthetic samples and adjusting the classification threshold to improve recall.”

5. What is your experience with Python in machine learning?

Python is a key programming language for machine learning, and your proficiency is essential.

How to Answer

Highlight your experience with Python libraries such as NumPy, pandas, and scikit-learn, and discuss how you have used them in your projects.

Example

“I have extensive experience using Python for machine learning, particularly with libraries like scikit-learn for model building and evaluation. I also utilize pandas for data manipulation and NumPy for numerical computations, which streamline my workflow.”

Behavioral Questions

1. Describe a time when you had to work as part of a team. What role did you play?

Teamwork is vital in collaborative environments like Cummins.

How to Answer

Share a specific example that illustrates your ability to work effectively in a team, focusing on your contributions and the outcome.

Example

“In a recent project, I collaborated with a cross-functional team to develop a predictive maintenance model. I took the lead in data preprocessing and model selection, ensuring that our approach aligned with the team’s goals, which ultimately led to a successful deployment.”

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

This question assesses your time management and organizational skills.

How to Answer

Discuss your approach to prioritization, including any tools or methods you use to manage your workload effectively.

Example

“I prioritize tasks based on deadlines and project impact. I use tools like Trello to visualize my workload and ensure I allocate time effectively. Regular check-ins with my team also help me stay aligned with project goals.”

3. Can you give an example of a conflict you faced in a team setting and how you resolved it?

Conflict resolution is an important skill in any collaborative role.

How to Answer

Describe a specific conflict, your approach to resolving it, and the positive outcome that resulted.

Example

“In a project, there was a disagreement about the choice of algorithm. I facilitated a discussion where each team member presented their viewpoint. By focusing on data-driven evidence, we reached a consensus on using a hybrid approach that combined the strengths of both algorithms.”

4. Why are you interested in working for Cummins?

This question gauges your motivation and alignment with the company’s values.

How to Answer

Express your interest in Cummins’ mission, values, and the specific role, connecting it to your career goals.

Example

“I am drawn to Cummins because of its commitment to innovation and sustainability in the engineering sector. I believe my skills in machine learning can contribute to developing solutions that enhance efficiency and reduce environmental impact.”

5. Where do you see yourself in five years?

This question assesses your career aspirations and alignment with the company’s growth.

How to Answer

Discuss your professional goals and how they align with the opportunities at Cummins.

Example

“In five years, I see myself as a senior machine learning engineer, leading projects that drive innovation. I am excited about the potential for growth at Cummins and contributing to impactful projects that align with my passion for technology and sustainability.”