In your interviews for the Computer Vision Engineer position, you can expect a variety of questions that assess both your technical expertise and your problem-solving abilities. The questions provided here are representative of what candidates have encountered during previous interviews at Anduril. They illustrate common themes and focus areas rather than serving as a memorization list.

Technical / Domain Questions

These questions will test your knowledge in computer vision, algorithms, and programming.

• Explain how convolutional neural networks (CNNs) work and their applications in computer vision.
• Describe the process of image segmentation and its importance in computer vision tasks.
• What is the difference between supervised and unsupervised learning? Provide examples specific to computer vision.
• How would you handle noise in image data?
• Describe a project where you implemented a computer vision solution. What challenges did you face?

Coding / Algorithms

Expect coding challenges that assess your programming skills and problem-solving approach.

• Write a function to detect edges in an image using the Sobel operator.
• Implement a simple image classification algorithm using a dataset of your choice.
• Solve a LeetCode-style problem related to data structures or algorithms relevant to image processing.
• Write a function that applies a Gaussian blur to an input image.
• Given a set of points, determine if they form a convex shape.

Behavioral / Leadership

These questions will help assess your fit within Anduril's culture and your ability to work in teams.

• Describe a time when you faced a significant challenge in a project. How did you overcome it?
• How do you prioritize tasks when working on multiple projects?
• Can you provide an example of how you worked collaboratively on a technical solution?
• Describe your approach to receiving and implementing feedback.
• How do you stay updated with the latest trends and advancements in computer vision?

Problem-Solving / Case Studies

These questions will evaluate your analytical skills and ability to approach complex problems.

• Given a scenario where a drone is required to navigate through an urban environment, how would you design the computer vision system?
• How would you approach optimizing an existing computer vision algorithm for real-time processing?
• Discuss a situation where you had to debug a complex computer vision application. What was your process?
• How would you assess the performance of a computer vision model?

System Design / Architecture

In some cases, questions may focus on the architectural design of systems.

• Design a computer vision system for a surveillance application. What components would you include?
• How would you structure a pipeline for processing video data in real-time?
• What considerations would you take into account when deploying a machine learning model in a production environment?

This visual timeline illustrates the stages of the interview process, helping you to manage your preparation and energy levels effectively. Each stage serves a purpose, allowing interviewers to assess both technical and interpersonal skills.

Deep Dive into Evaluation Areas

Understanding how you will be evaluated during the interview is crucial for effective preparation. Below are key evaluation areas that Anduril focuses on when interviewing candidates for the Computer Vision Engineer role.

Technical Proficiency

Your technical skills in computer vision and programming will be rigorously assessed. Interviewers will evaluate your understanding of algorithms, data structures, and specific computer vision techniques.

• Computer Vision Fundamentals – Knowledge of foundational concepts like image processing, feature extraction, and model evaluation.
• Programming Skills – Proficiency in languages such as Python or C++ and familiarity with relevant libraries (e.g., OpenCV, TensorFlow).
• Machine Learning – Understanding of machine learning principles applied to vision tasks, including neural networks and training methodologies.

Example questions or scenarios:

• Describe how you would implement object detection in a video feed.
• Explain the role of transfer learning in computer vision applications.

Problem-Solving Skills

Your ability to approach complex problems systematically will be evaluated through coding challenges and case studies.

• Analytical Thinking – Demonstrating a structured approach to solving problems and articulating your thought process.
• Debugging Skills – Ability to identify and resolve issues in code and algorithms effectively.
• Efficiency – Understanding of optimizing solutions for performance and scalability.

Example questions or scenarios:

• Given a performance bottleneck in your algorithm, how would you diagnose and resolve it?
• Describe a time you had to refactor code for better performance.

Collaboration and Communication

Anduril values candidates who can work effectively within teams and communicate complex ideas clearly.

• Teamwork – Your experience working in collaborative environments and how you handle group dynamics.
• Communication – Ability to convey technical concepts to non-technical stakeholders and articulate your ideas clearly during interviews.

Example questions or scenarios:

• Discuss a time when you successfully collaborated on a project. What role did you play?
• How do you approach explaining technical concepts to team members who may not be familiar with the subject?

Innovation and Adaptability

Your ability to innovate and adapt to changing conditions will be assessed through scenario-based questions.

• Creative Problem Solving – Your capacity to think outside the box and propose novel solutions.
• Flexibility – How you adapt to new technologies and methodologies in a fast-paced environment.

Example questions or scenarios:

• Describe a project where you had to learn a new technology quickly. How did you approach it?
• How would you adapt a computer vision model for a different application than originally intended?