Oshkosh Corporation Machine Learning Engineer Interview Questions + Guide in 2025

Oshkosh Corporation specializes in designing and manufacturing some of the toughest specialty trucks and access equipment, serving communities and protecting lives globally.

As a Machine Learning Engineer at Oshkosh, you will be at the forefront of revolutionizing transport and logistics through cutting-edge human-machine collaboration for mission-critical vehicles. Your responsibilities will include designing, implementing, and testing scalable distributed data systems in the cloud, while championing engineering excellence. You will also generate platforms to support machine learning model training, evaluation, and deployment workflows, and own the technical project lifecycles that contribute to the team’s product roadmap. With a focus on mentorship, you will support junior professionals in their onboarding and development.

To excel in this role, you should possess a strong foundation in algorithms, with a specific emphasis on Python for building scalable infrastructure. Experience in machine learning and artificial intelligence is crucial, as well as familiarity with SQL for analytics. Your ability to navigate complex technical challenges, communicate effectively, and demonstrate a commitment to Oshkosh's values—putting people first, doing the right thing, and persevering—will be key to your success.

This guide will provide you with insights into the role and the types of questions you may encounter, helping you prepare effectively for your interview and stand out as a candidate.

The interview process for a Machine Learning Engineer at Oshkosh Corporation is structured to assess both technical expertise and cultural fit within the organization. It typically consists of several stages, each designed to evaluate different aspects of a candidate's qualifications and alignment with the company's values.

1. Initial Phone Screen

The first step in the interview process is a phone screen with a recruiter. This conversation is generally relaxed and serves as an opportunity for candidates to discuss their backgrounds, experiences, and motivations for applying to Oshkosh. Expect to answer behavioral questions that explore your strengths, conflict resolution strategies, and relevant projects. The recruiter will also provide insights into the company culture and the specifics of the role.

2. Technical Interview

Following the initial screen, candidates typically participate in a technical interview. This may be conducted virtually and often involves a panel of interviewers, including the hiring manager and other team members. During this stage, candidates are expected to demonstrate their technical knowledge in machine learning, algorithms, and programming languages such as Python. You may be asked to explain your previous projects in detail, focusing on the technologies used and the outcomes achieved. Additionally, expect questions that assess your problem-solving skills and your ability to apply machine learning concepts to real-world scenarios.

3. Onsite Interview

The final stage of the interview process usually involves an onsite interview, where candidates meet with multiple team members. This round is more in-depth and may include a mix of technical assessments and behavioral questions. Candidates might be asked to present their work or projects, showcasing their understanding of machine learning principles and their ability to communicate complex ideas effectively. The interviewers will also evaluate how well you align with Oshkosh's core values and culture, emphasizing collaboration and innovation.

Throughout the process, candidates should be prepared for a friendly yet professional environment, where interviewers are keen to learn about your experiences and how you can contribute to the team.

Now, let's delve into the specific interview questions that candidates have encountered during their interviews at Oshkosh Corporation.

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

Embrace the People-First Culture

Oshkosh Corporation prides itself on a people-first culture, so be sure to convey your alignment with this value during your interview. Share examples of how you have prioritized teamwork, collaboration, and mentorship in your previous roles. Highlight your ability to support junior professionals and foster a positive work environment, as this will resonate well with the interviewers.

Prepare for Behavioral Questions

Expect a mix of behavioral questions that assess your problem-solving skills and how you handle pressure. Prepare to discuss your strengths and provide specific examples of times you faced challenges, such as working under tight deadlines or resolving conflicts. The interviewers appreciate a conversational tone, so approach these questions as an opportunity to share your story rather than a formal interrogation.

Showcase Your Technical Expertise

Given the technical nature of the Machine Learning Engineer role, be ready to discuss your experience with algorithms, Python, and machine learning frameworks. Prepare to explain your projects in detail, focusing on the technical challenges you faced and how you overcame them. Familiarize yourself with the latest trends in machine learning and be prepared to discuss how they can be applied to Oshkosh's mission of enhancing safety and performance in autonomous vehicles.

Understand the Company’s Mission

Oshkosh is dedicated to building and protecting communities through innovative engineering. Familiarize yourself with their products and the impact they have on various sectors, such as defense and emergency services. This knowledge will allow you to connect your skills and experiences to the company's goals, demonstrating your genuine interest in contributing to their mission.

Be Ready for a Panel Interview

If you progress to a panel interview, expect to engage with multiple team members. This format can be intimidating, but remember that it’s an opportunity to showcase your ability to communicate effectively with diverse stakeholders. Practice articulating your thoughts clearly and concisely, and be prepared to answer questions from different perspectives.

Ask Insightful Questions

Prepare thoughtful questions that reflect your interest in the role and the company. Inquire about the team dynamics, ongoing projects, and how the Machine Learning group collaborates with other departments. This not only shows your enthusiasm but also helps you gauge if the company culture aligns with your values.

Follow Up Professionally

After your interview, send a thank-you email to express your appreciation for the opportunity to interview. Reiterate your interest in the role and briefly mention a key point from your conversation that reinforces your fit for the position. This small gesture can leave a lasting impression and demonstrate your professionalism.

By following these tips, you will be well-prepared to navigate the interview process at Oshkosh Corporation and showcase your potential as a Machine Learning Engineer. Good luck!

In this section, we’ll review the various interview questions that might be asked during an interview for a Machine Learning Engineer position at Oshkosh Corporation. The interview process will likely focus on your technical expertise in machine learning, algorithms, and programming, as well as your ability to work collaboratively in a team-oriented environment. Be prepared to discuss your past experiences, technical projects, and how you can contribute to the company's mission of innovation in transport and logistics.

Machine Learning

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

Discuss the definitions of both supervised and unsupervised learning, providing examples of each. Highlight the types of problems each approach is best suited for.

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 or groupings, like clustering customers based on purchasing behavior.”

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

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

How to Answer

Detail the project, your role, the technologies used, and the specific challenges encountered. Emphasize how you overcame these challenges.

Example

“I worked on a project to develop a predictive maintenance model for industrial equipment. One challenge was dealing with imbalanced data, which I addressed by implementing SMOTE to generate synthetic samples. This improved the model's accuracy significantly.”

3. How do you evaluate the performance of a machine learning model?

This question tests your understanding of model evaluation metrics.

How to Answer

Discuss various metrics such as accuracy, precision, recall, F1 score, and ROC-AUC. Explain when to use each metric based on the problem context.

Example

“I evaluate model performance using accuracy for balanced datasets, but for imbalanced datasets, I prefer precision and recall. For instance, in a fraud detection model, high recall is crucial to minimize false negatives, so I focus on optimizing that metric.”

4. What techniques do you use to prevent overfitting in your models?

This question gauges your knowledge of model generalization.

How to Answer

Mention techniques like cross-validation, regularization, and pruning. Provide examples of how you have applied these techniques in past projects.

Example

“To prevent overfitting, I often use cross-validation to ensure my model performs well on unseen data. Additionally, I apply L1 and L2 regularization to penalize overly complex models, which helps maintain generalization.”

5. Can you explain the concept of feature engineering and its importance?

This question assesses your understanding of data preprocessing.

How to Answer

Define feature engineering and discuss its role in improving model performance. Provide examples of techniques you have used.

Example

“Feature engineering involves creating new input features from existing data to improve model performance. For instance, in a sales prediction model, I created a feature for the day of the week to capture seasonal trends, which significantly enhanced the model's accuracy.”

Algorithms

1. What is the difference between a decision tree and a random forest?

This question tests your knowledge of algorithms used in machine learning.

How to Answer

Explain the basic structure of both algorithms and their differences in terms of performance and use cases.

Example

“A decision tree is a single tree structure that makes decisions based on feature splits, while a random forest is an ensemble of multiple decision trees that improves accuracy by averaging their predictions. Random forests are less prone to overfitting compared to single decision trees.”

2. How do you handle missing data in a dataset?

This question evaluates your data preprocessing skills.

How to Answer

Discuss various strategies for handling missing data, such as imputation, deletion, or using algorithms that support missing values.

Example

“I handle missing data by first analyzing the extent and pattern of the missingness. If it's minimal, I might use mean or median imputation. For larger gaps, I consider using algorithms like k-NN that can handle missing values directly.”

3. Can you explain the bias-variance tradeoff?

This question assesses your understanding of model performance.

How to Answer

Define bias and variance, and explain how they relate to model complexity and generalization.

Example

“The bias-variance tradeoff refers to the balance between a model's ability to minimize bias, which leads to underfitting, and variance, which leads to overfitting. A good model achieves a balance where both bias and variance are minimized, ensuring good generalization to unseen data.”

4. What is gradient descent, and how does it work?

This question tests your understanding of optimization techniques.

How to Answer

Explain the concept of gradient descent and its role in training machine learning models.

Example

“Gradient descent is an optimization algorithm used to minimize the loss function by iteratively adjusting model parameters in the direction of the steepest descent. It works by calculating the gradient of the loss function and updating the parameters accordingly until convergence.”

5. Describe a time when you had to optimize an algorithm for performance.

This question assesses your practical experience with algorithm optimization.

How to Answer

Provide a specific example of an algorithm you optimized, the challenges faced, and the results achieved.

Example

“I optimized a recommendation algorithm that was running too slowly by implementing a caching mechanism for frequently accessed data. This reduced the response time by 50%, significantly improving user experience.”

Programming and Tools

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

This question evaluates your technical skills and experience.

How to Answer

List the programming languages you are proficient in, particularly Python, and provide examples of how you have used them in machine learning projects.

Example

“I am proficient in Python, which I used extensively for data analysis and building machine learning models using libraries like Pandas, NumPy, and Scikit-learn. I also have experience with SQL for data manipulation and retrieval.”

2. How do you ensure code quality and maintainability in your projects?

This question assesses your coding practices.

How to Answer

Discuss practices such as code reviews, unit testing, and documentation that you implement to maintain code quality.

Example

“I ensure code quality by conducting regular code reviews with my team and writing unit tests to validate functionality. Additionally, I maintain thorough documentation to make it easier for others to understand and build upon my work.”

3. Can you describe your experience with cloud platforms for machine learning?

This question evaluates your familiarity with cloud technologies.

How to Answer

Mention specific cloud platforms you have used, the services you utilized, and how they contributed to your projects.

Example

“I have experience using AWS for deploying machine learning models. I utilized services like S3 for data storage and SageMaker for training and deploying models, which streamlined the workflow and improved scalability.”

4. What tools do you use for data visualization, and why are they important?

This question assesses your data visualization skills.

How to Answer

Discuss the tools you use for data visualization and their importance in communicating insights.

Example

“I primarily use Matplotlib and Seaborn for data visualization in Python. These tools are essential for exploring data patterns and presenting results to stakeholders in an understandable format, which aids in decision-making.”

5. How do you stay updated with the latest trends and advancements in machine learning?

This question evaluates your commitment to continuous learning.

How to Answer

Mention resources such as online courses, research papers, and conferences that you utilize to stay informed.

Example

“I stay updated by following leading machine learning journals, participating in online courses on platforms like Coursera, and attending industry conferences. This helps me keep abreast of the latest advancements and best practices in the field.”