CACI International Inc Machine Learning Engineer Interview Questions + Guide in 2025

CACI International Inc is a leading provider of technology solutions and services to the federal government, focusing on mission-critical applications and systems to ensure national security and operational effectiveness.

As a Machine Learning Engineer at CACI, you will be responsible for developing advanced machine learning solutions and data pipelines, specifically tailored for intelligence applications. This role requires you to rapidly prototype containerized multimodal deep learning models while utilizing state-of-the-art Computer Vision (CV) and Vision Language Models (VLM). You will work with complex geospatial datasets, such as satellite imagery and full-motion video, to enhance analytic workflows and address key intelligence questions.

Key responsibilities include applying transfer learning and knowledge distillation methodologies to optimize pre-trained models for segmentation and object detection tasks. You will also be expected to build secure containerized Python applications, automate builds using CI/CD pipelines, and interact with S3 compliant APIs for data retrieval and preprocessing.

The ideal candidate will possess significant experience in deep learning frameworks like PyTorch or TensorFlow, as well as a strong understanding of version control systems such as GitLab. Familiarity with CUDA for GPU-accelerated computing is essential, and a current TS/SCI security clearance is required. Additional experience with container orchestration platforms, like Kubernetes, and knowledge of the HuggingFace Transformers library will be advantageous.

This guide aims to equip candidates with the knowledge and confidence to excel in their interviews for the Machine Learning Engineer role at CACI by providing insights into the expectations and required skills for success.

The interview process for a Machine Learning Engineer at CACI is structured to assess both technical expertise and cultural fit within the organization. It typically consists of several stages designed to evaluate your skills in machine learning, programming, and problem-solving, as well as your ability to work collaboratively in a team environment.

1. Initial Phone Screen

The process begins with an initial phone screen, usually conducted by a recruiter. This conversation lasts about 30-45 minutes and focuses on your background, experience, and motivation for applying to CACI. Expect questions about your resume, your understanding of the role, and your familiarity with machine learning concepts. This is also an opportunity for you to ask about the company culture and the specifics of the position.

2. Technical Interview

Following the initial screen, candidates typically participate in a technical interview. This may be conducted over video conferencing platforms like Teams or Zoom. During this interview, you will be asked to solve coding problems and answer technical questions related to machine learning frameworks, algorithms, and programming languages such as Python. You may also be required to demonstrate your understanding of deep learning concepts, including experience with frameworks like PyTorch or TensorFlow.

3. Coding Assessment

In some cases, candidates are given a coding assignment to complete before the next round of interviews. This assignment usually involves practical tasks related to machine learning, such as building a model or processing data. You will typically have a limited time to complete this task, and it will be evaluated based on your coding style, efficiency, and the correctness of your solution.

4. Panel Interview

The final stage often includes a panel interview with multiple team members, including managers and potential colleagues. This interview is more conversational and may include behavioral questions to assess your teamwork and problem-solving skills. You can expect questions about past projects, challenges you've faced, and how you approach collaboration in a team setting. This is also a chance for you to showcase your communication skills and how you articulate complex technical concepts.

5. Final Discussion

If you successfully navigate the previous stages, you may have a final discussion with a senior leader or hiring manager. This conversation often focuses on your long-term career goals, alignment with CACI's mission, and any remaining questions you may have about the role or the company.

As you prepare for your interview, be ready to discuss your technical skills in detail, particularly in areas such as algorithms, Python programming, and machine learning methodologies.

Next, let's delve into the specific interview questions that candidates have encountered during the process.

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

Understand the Company Culture

CACI emphasizes a culture of integrity, trust, and continuous growth. Familiarize yourself with their core values and how they align with your own. Be prepared to discuss how your personal values resonate with CACI's mission to ensure national safety. This understanding will not only help you answer questions more effectively but also demonstrate your genuine interest in being part of their team.

Prepare for Technical Proficiency

Given the role's focus on machine learning, algorithms, and Python, ensure you are well-versed in these areas. Brush up on your knowledge of deep learning frameworks like PyTorch and TensorFlow, as well as containerization and CI/CD practices. Be ready to discuss your experience with transfer learning, knowledge distillation, and any relevant projects you've worked on. Practice coding problems that involve algorithms and data structures, as these are likely to come up during technical assessments.

Showcase Your Problem-Solving Skills

During the interview, you may encounter scenario-based questions that assess your problem-solving abilities. Prepare to discuss specific challenges you've faced in previous projects, how you approached them, and the outcomes. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you highlight your analytical thinking and technical skills.

Be Ready for Behavioral Questions

Expect a mix of behavioral and technical questions. CACI values teamwork and collaboration, so be prepared to discuss your experiences working in teams, how you handle conflicts, and your approach to communication. Reflect on past experiences where you demonstrated leadership, adaptability, and a commitment to continuous learning.

Engage with Your Interviewers

The interview process at CACI is described as friendly and conversational. Take the opportunity to engage with your interviewers by asking insightful questions about the team, projects, and company culture. This not only shows your interest but also helps you assess if CACI is the right fit for you.

Prepare for a Panel Interview

If you are invited to a panel interview, be ready to address multiple interviewers at once. Practice maintaining eye contact and addressing each panel member as you respond to their questions. This will help you appear confident and engaged, making a positive impression on the team.

Follow Up

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

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

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at CACI International Inc. Candidates should focus on demonstrating their technical expertise, problem-solving abilities, and experience with machine learning frameworks and methodologies. Be prepared to discuss your past projects, the challenges you faced, and how you overcame them.

Machine Learning

1. Can you explain the concept of transfer learning and how you have applied it in your projects?

Transfer learning is a technique where a pre-trained model is fine-tuned on a new task with limited data. Discuss specific projects where you utilized transfer learning, the models you used, and the outcomes achieved.

How to Answer

Provide a clear definition of transfer learning and describe a specific instance where you applied it, including the model architecture and the results.

Example

“In my last project, I used transfer learning with a pre-trained ResNet model to classify satellite images. By fine-tuning the model on a smaller dataset, I improved accuracy from 70% to 85%, demonstrating the effectiveness of leveraging existing models for new tasks.”

2. Describe your experience with deep learning frameworks such as TensorFlow or PyTorch.

This question assesses your familiarity with popular deep learning libraries and your ability to implement machine learning solutions.

How to Answer

Discuss specific projects where you used these frameworks, the types of models you built, and any challenges you faced.

Example

“I have extensive experience with PyTorch, particularly in developing convolutional neural networks for image segmentation tasks. I built a U-Net model for a project involving medical imaging, which significantly improved the segmentation accuracy compared to traditional methods.”

3. How do you approach debugging a machine learning model that is underperforming?

This question evaluates your problem-solving skills and understanding of model evaluation.

How to Answer

Outline a systematic approach to debugging, including data validation, model architecture review, and hyperparameter tuning.

Example

“When faced with an underperforming model, I first check the data for quality and distribution. Then, I analyze the model’s architecture and performance metrics, adjusting hyperparameters as needed. For instance, in a recent project, I discovered that increasing the learning rate improved convergence significantly.”

4. Can you explain the difference between object detection and image segmentation?

This question tests your understanding of key concepts in computer vision.

How to Answer

Clearly define both terms and provide examples of when each technique is used.

Example

“Object detection involves identifying and localizing objects within an image, while image segmentation classifies each pixel into a category. For example, in a satellite imagery project, I used object detection to locate buildings and image segmentation to delineate land use types.”

5. What techniques do you use for model validation and testing?

This question assesses your knowledge of best practices in machine learning.

How to Answer

Discuss various validation techniques, such as cross-validation, and how you apply them to ensure model robustness.

Example

“I typically use k-fold cross-validation to assess model performance, ensuring that the model generalizes well to unseen data. In a recent project, this approach helped identify overfitting, leading to adjustments in the model architecture.”

Programming and Tools

1. Describe your experience with containerization and CI/CD pipelines.

This question evaluates your familiarity with modern software development practices.

How to Answer

Discuss specific tools you’ve used for containerization and CI/CD, and how they improved your workflow.

Example

“I have used Docker to containerize machine learning applications, which streamlined deployment across different environments. Additionally, I implemented CI/CD pipelines using GitLab CI, automating testing and deployment processes, which reduced integration issues significantly.”

2. How do you handle data preprocessing in your machine learning projects?

This question assesses your understanding of data preparation, which is crucial for model performance.

How to Answer

Outline your typical data preprocessing steps, including handling missing values, normalization, and feature engineering.

Example

“I start by exploring the dataset for missing values and outliers. I then normalize the data and apply techniques like one-hot encoding for categorical variables. In a recent project, I engineered features that improved model performance by 15%.”

3. Can you explain how you would optimize a convolutional neural network?

This question tests your knowledge of model optimization techniques.

How to Answer

Discuss various strategies, such as adjusting learning rates, using dropout, and data augmentation.

Example

“To optimize a CNN, I would start by experimenting with different learning rates using a learning rate scheduler. I also implement dropout layers to prevent overfitting and use data augmentation techniques to increase the diversity of the training set.”

4. What is your experience with version control systems like Git?

This question evaluates your ability to manage code and collaborate with others.

How to Answer

Discuss your experience with Git, including branching strategies and collaboration practices.

Example

“I regularly use Git for version control, employing a branching strategy that allows for feature development without disrupting the main codebase. This practice has facilitated smoother collaboration with my team on various projects.”

5. How do you ensure the security of your machine learning applications?

This question assesses your awareness of security practices in software development.

How to Answer

Discuss specific security measures you implement, such as data encryption and secure coding practices.

Example

“I prioritize security by implementing data encryption both at rest and in transit. Additionally, I follow secure coding practices to mitigate vulnerabilities, ensuring that sensitive data is protected throughout the application lifecycle.”