Leidos Machine Learning Engineer Interview Questions + Guide in 2025

Leidos is a leading technology company that provides solutions for national security, engineering, and IT services.

As a Machine Learning Engineer at Leidos, you will take a pivotal role in designing, developing, and maintaining advanced AI and machine learning tools utilized in the analysis and design of high-speed weapon systems. You will be expected to leverage your expertise in algorithms and programming to propose innovative research ideas while collaborating closely with aerospace, mechanical, and electrical engineers. This role requires a strong command of machine learning frameworks such as TensorFlow and PyTorch, along with proficiency in programming languages like Python and C/C++. Effective communication skills are crucial, as you will interact with internal teams and external clients to present research findings and ensure alignment with project goals.

Candidates who thrive in a collaborative, close-knit team environment and possess a passion for continuous learning will excel in this role. The position not only demands technical prowess but also the ability to publish research papers and present at conferences, showcasing your contributions to the field of AI and machine learning. This guide will equip you with the specific knowledge and insights necessary to prepare effectively for your interview at Leidos, enhancing your chances of success.

The interview process for a Machine Learning Engineer at Leidos is structured to assess both technical and interpersonal skills, ensuring candidates are well-suited for the collaborative and innovative environment of the company. The process typically unfolds in several stages:

1. Initial Phone Screening

The first step is a phone screening conducted by an HR representative. This conversation usually lasts about 30 minutes and focuses on your background, experience, and motivation for applying to Leidos. Expect to discuss your resume in detail, including your technical skills and relevant projects. This is also an opportunity for you to ask questions about the company culture and the specifics of the role.

2. Technical Interview

Following the initial screening, candidates typically participate in a technical interview, which may be conducted via video conferencing. This interview usually lasts around 45 minutes to an hour and is led by a technical team member or a hiring manager. During this session, you can expect to tackle coding problems, algorithm questions, and discussions around machine learning concepts. Be prepared to demonstrate your proficiency in programming languages such as Python, as well as your understanding of machine learning frameworks like TensorFlow or PyTorch.

3. Panel Interview

The next stage often involves a panel interview, which includes multiple team members, such as engineers and project managers. This round can last up to an hour and typically combines both technical and behavioral questions. You may be asked to explain your past projects, discuss your approach to problem-solving, and how you handle challenges in a team setting. This is also a chance to showcase your communication skills, as you will need to articulate complex technical concepts clearly.

4. Final Interview

In some cases, a final interview may be conducted with senior management or team leads. This round focuses on assessing your fit within the team and the company culture. Expect questions about your long-term career goals, how you align with Leidos' mission, and your ability to work collaboratively in a close-knit team environment. This is also an opportunity for you to ask more in-depth questions about the projects you would be working on and the team dynamics.

5. Offer and Background Check

If you successfully navigate the interview rounds, you may receive a job offer. However, be aware that Leidos requires candidates to undergo a background check and, in some cases, obtain a security clearance. This process can take additional time, so patience is key.

As you prepare for your interview, consider the types of questions that may arise in each of these stages, particularly those that relate to your technical expertise and collaborative experiences.

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

Understand the Role and Company Culture

Before your interview, take the time to familiarize yourself with Leidos' mission, values, and recent projects, especially those related to AI and machine learning. Given the collaborative nature of the work, understanding how your role as a Machine Learning Engineer fits into the larger team and company objectives will help you articulate your potential contributions. Emphasize your enthusiasm for working in a close-knit team environment where creativity and continuous learning are valued.

Prepare for Technical Questions

Given the emphasis on algorithms and machine learning in this role, ensure you are well-versed in relevant concepts. Brush up on your knowledge of deep learning frameworks such as TensorFlow, Keras, and PyTorch, as well as your programming skills in Python and C/C++. Be prepared to discuss your experience with algorithms, particularly in the context of developing and maintaining artificial neural networks. Practice coding problems that may involve data structures and algorithms, as well as any specific machine learning techniques you have used in past projects.

Showcase Your Communication Skills

Leidos values effective communication, especially in a role that requires collaboration with engineers and external customers. Be ready to discuss how you have successfully communicated complex technical concepts to non-technical stakeholders in the past. Prepare examples that demonstrate your ability to present research ideas clearly and concisely, both verbally and in writing. This will be crucial, as the role involves preparing manuscripts for publication and presenting at conferences.

Highlight Relevant Experience

During the interview, be sure to discuss your past projects and experiences that align with the job description. Focus on your contributions to AI/ML projects, particularly those that involved modeling aerodynamic, structural, or thermal phenomena. If you have experience with modeling and simulation disciplines like Computational Fluid Dynamics (CFD) or Finite Element Analysis (FEA), be sure to highlight that as well. Tailor your responses to reflect how your background makes you a strong fit for the specific challenges Leidos is addressing.

Be Ready for Behavioral Questions

Expect a mix of technical and behavioral questions during your interview. Prepare for common behavioral inquiries, such as how you handle challenges or work with difficult team members. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you provide clear and concise examples that demonstrate your problem-solving skills and ability to work collaboratively.

Ask Insightful Questions

At the end of your interview, take the opportunity to ask thoughtful questions about the team dynamics, ongoing projects, and the future direction of AI/ML initiatives at Leidos. This not only shows your genuine interest in the role but also helps you assess if the company culture aligns with your values and career goals.

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

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at Leidos. The interview process will likely focus on your technical expertise in machine learning, programming skills, and your ability to communicate effectively with team members and clients. Be prepared to discuss your past experiences, technical knowledge, and how you can contribute to the team.

Machine Learning

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

Understanding the fundamental concepts of machine learning is crucial. Be clear about the definitions and provide examples of each type.

How to Answer

Discuss the key characteristics of both supervised and unsupervised learning, including the types of problems each is 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, where the model tries to find patterns or groupings, like clustering customers based on purchasing behavior.”

2. What is transfer learning, and how have you applied it in your projects?

Transfer learning is a significant concept in machine learning, especially in deep learning.

How to Answer

Explain the concept of transfer learning and provide a specific example of how you have used it in a project.

Example

“Transfer learning allows us to leverage pre-trained models on similar tasks to improve performance and reduce training time. In my last project, I used a pre-trained ResNet model for image classification, fine-tuning it on a smaller dataset of medical images, which significantly improved accuracy.”

3. Describe your experience with deep learning frameworks like TensorFlow or PyTorch.

Familiarity with popular frameworks is essential for a Machine Learning Engineer.

How to Answer

Discuss your experience with these frameworks, including specific projects or tasks you have completed.

Example

“I have extensive experience with TensorFlow, where I built and trained convolutional neural networks for image recognition tasks. I appreciate its flexibility and the ability to deploy models easily in production environments.”

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

Understanding model evaluation metrics is critical for assessing model effectiveness.

How to Answer

Mention various metrics and techniques used for evaluation, such as accuracy, precision, recall, F1 score, and ROC-AUC.

Example

“I evaluate model performance using metrics like accuracy for classification tasks, and I also consider precision and recall to understand the trade-offs. For imbalanced datasets, I prefer using the F1 score and ROC-AUC to get a better sense of the model's performance.”

Programming and Algorithms

1. What programming languages are you most comfortable using, and why?

This question assesses your programming proficiency, which is vital for the role.

How to Answer

Mention the languages you are proficient in and relate them to your experience in machine learning.

Example

“I am most comfortable with Python due to its extensive libraries for data analysis and machine learning, such as NumPy, Pandas, and scikit-learn. I also have experience with C++ for performance-critical applications.”

2. Can you explain the difference between stack and heap memory?

This question tests your understanding of memory management, which is important for efficient programming.

How to Answer

Provide a clear distinction between stack and heap memory, including their uses and characteristics.

Example

“Stack memory is used for static memory allocation, where variables are stored in a last-in, first-out manner, making it faster but limited in size. Heap memory, on the other hand, is used for dynamic memory allocation, allowing for more flexible memory usage but with slower access times.”

3. Describe a time you optimized a machine learning model. What techniques did you use?

This question evaluates your problem-solving skills and understanding of optimization techniques.

How to Answer

Discuss specific techniques you used to improve model performance, such as hyperparameter tuning, feature selection, or using different algorithms.

Example

“In a recent project, I optimized a model by performing hyperparameter tuning using grid search and cross-validation. I also implemented feature selection techniques to reduce dimensionality, which improved the model's accuracy and reduced overfitting.”

4. What is your experience with object-oriented programming (OOP)?

OOP principles are often used in software development, including machine learning applications.

How to Answer

Explain your understanding of OOP concepts and how you have applied them in your projects.

Example

“I have a solid understanding of OOP principles such as encapsulation, inheritance, and polymorphism. I applied these concepts in a project where I developed a modular machine learning pipeline, allowing for easy updates and maintenance of different components.”

Communication and Collaboration

1. How do you approach communicating complex technical concepts to non-technical stakeholders?

Effective communication is key in a collaborative environment.

How to Answer

Discuss your strategies for simplifying complex ideas and ensuring understanding.

Example

“I focus on using analogies and visual aids to explain complex concepts. For instance, when discussing model performance, I might use a simple graph to illustrate accuracy versus recall, making it easier for non-technical stakeholders to grasp the implications.”

2. Describe a time you faced a challenge while working in a team. How did you handle it?

This question assesses your teamwork and conflict resolution skills.

How to Answer

Provide a specific example of a challenge and how you worked with your team to resolve it.

Example

“In a previous project, there was a disagreement on the choice of algorithms. I facilitated a meeting where each team member presented their rationale, and we collectively decided to run experiments on both approaches. This not only resolved the conflict but also led to a better-informed decision.”

3. How do you ensure that your work aligns with customer goals and objectives?

Understanding customer needs is crucial for project success.

How to Answer

Discuss your methods for gathering requirements and feedback from customers.

Example

“I ensure alignment with customer goals by conducting regular check-ins and gathering feedback throughout the project lifecycle. I also make it a point to clarify objectives at the start and adjust our approach based on their input.”

4. Can you provide an example of a research paper you published or presented?

This question evaluates your ability to contribute to the academic community.

How to Answer

Mention the topic of your research, the publication or conference, and its significance.

Example

“I published a paper on the application of deep learning in predictive maintenance at the IEEE Conference on Machine Learning. The research demonstrated how our model could reduce downtime in manufacturing processes, which garnered interest from industry professionals.”