Essential Work Sample Exercises for Evaluating Applied Computer Vision Skills

Applied Computer Vision represents one of the most rapidly evolving and impactful areas of modern technology. From autonomous vehicles to medical diagnostics, facial recognition to industrial quality control, computer vision applications are transforming industries and creating new possibilities. However, identifying candidates with genuine expertise in this field presents unique challenges for hiring managers and technical teams.

Traditional interviews often fail to reveal a candidate's true capabilities in applied computer vision. While a resume might list impressive qualifications and experience with relevant frameworks, these credentials don't necessarily translate to practical problem-solving abilities or the capacity to implement efficient, scalable solutions. The gap between theoretical knowledge and practical application is particularly pronounced in this field.

Work sample exercises provide a window into how candidates approach real-world computer vision challenges. They reveal not just technical proficiency with libraries and algorithms, but also critical thinking, problem decomposition, and the ability to balance theoretical ideals with practical constraints. These exercises allow hiring teams to observe how candidates handle ambiguity, optimize for performance, and communicate complex technical concepts.

The following four exercises are designed to evaluate different facets of applied computer vision expertise. They range from tactical implementation to strategic planning, debugging to optimization. By incorporating these exercises into your hiring process, you'll gain deeper insights into candidates' capabilities and identify those who can truly drive your computer vision initiatives forward.

Activity #1: Image Processing Pipeline Implementation

This exercise evaluates a candidate's ability to implement fundamental image processing techniques that form the foundation of most computer vision applications. It tests their understanding of preprocessing steps, feature extraction, and basic algorithmic implementation without relying entirely on high-level APIs.

Directions for the Company:

• Provide the candidate with a set of 5-10 sample images (preferably related to your industry) and a clear problem statement.
• The problem should involve implementing a multi-step image processing pipeline (e.g., noise reduction, feature detection, segmentation, or classification).
• Provide access to a development environment with common libraries (OpenCV, NumPy, etc.) but specify that certain components must be implemented without using high-level functions.
• Allow 60-90 minutes for this exercise.
• Prepare sample images that have clear features relevant to the task.
• Consider providing a skeleton code structure to save time on boilerplate setup.

Directions for the Candidate:

• Implement an image processing pipeline to solve the specified problem using the provided sample images.
• Your solution should include preprocessing steps (e.g., noise reduction, normalization), feature extraction, and a final processing/classification step.
• Implement at least one key algorithm component from scratch rather than using a built-in function.
• Document your approach, explaining the rationale behind your design choices and any trade-offs you considered.
• Be prepared to explain how your solution would scale to larger datasets or different image conditions.

Feedback Mechanism:

• After reviewing the implementation, provide feedback on algorithm choice, code quality, and effectiveness of the solution.
• Highlight one aspect the candidate implemented particularly well and one area for improvement.
• Ask the candidate to refine a specific component of their pipeline based on the feedback, allowing 15-20 minutes for this refinement.
• Observe how they incorporate feedback and whether they can articulate the expected impact of their changes.

Activity #2: Computer Vision System Design

This exercise assesses a candidate's ability to architect a complete computer vision system, demonstrating their understanding of the entire pipeline from data acquisition to deployment. It reveals their knowledge of system integration, scalability considerations, and practical implementation constraints.

Directions for the Company:

• Prepare a realistic business scenario requiring a computer vision solution (e.g., warehouse inventory tracking, medical image analysis, retail customer analytics).
• Provide relevant constraints such as hardware limitations, accuracy requirements, latency expectations, and scale.
• Create a whiteboard or digital drawing tool environment for the candidate.
• Allow 45-60 minutes for this exercise.
• Prepare specific questions about trade-offs, failure modes, and scaling considerations to probe the depth of the candidate's understanding.
• Have a technical team member familiar with system architecture participate in the evaluation.

Directions for the Candidate:

• Design a complete computer vision system architecture to address the business scenario.
• Your design should include:
• Data acquisition and preprocessing components
• Core algorithm selection and implementation approach
• Deployment architecture (edge vs. cloud processing)
• Scaling considerations
• Monitoring and maintenance strategy
• Create a diagram showing the system components and data flow.
• Be prepared to explain your technical choices and discuss alternative approaches.
• Address how your design handles edge cases, failures, and performance bottlenecks.

Feedback Mechanism:

• Provide feedback on the completeness, practicality, and innovation of the design.
• Highlight one particularly strong aspect of their architecture and one area that could be improved.
• Introduce a new constraint or requirement (e.g., "What if we needed to process images 10x faster?" or "What if we had to run this on edge devices with limited memory?").
• Give the candidate 15 minutes to adapt their design to accommodate this new constraint.
• Evaluate their flexibility and problem-solving approach when faced with changing requirements.

Activity #3: Computer Vision Debugging and Optimization

This exercise evaluates a candidate's ability to troubleshoot and optimize existing computer vision code, demonstrating their debugging skills, performance optimization knowledge, and familiarity with common pitfalls in computer vision implementations.

Directions for the Company:

• Prepare a functional but suboptimal computer vision implementation with several issues:
• Performance bottlenecks
• Memory inefficiencies
• Accuracy problems
• Poor code organization
• The code should use common libraries (OpenCV, TensorFlow, PyTorch) and implement a realistic task.
• Provide documentation explaining the intended functionality and current performance metrics.
• Allow 60-90 minutes for this exercise.
• Include both obvious issues and subtle problems that require deeper understanding.
• Prepare a testing environment where candidates can measure the impact of their changes.

Directions for the Candidate:

• Review the provided computer vision implementation and identify issues affecting its performance, accuracy, or maintainability.
• Implement improvements to address the identified issues while maintaining the core functionality.
• Document each issue you find and explain your approach to fixing it.
• Measure and report the impact of your changes on performance and accuracy.
• Prioritize your improvements based on their expected impact and implementation difficulty.
• Be prepared to explain the theoretical principles behind your optimizations.

Feedback Mechanism:

• After reviewing their changes, highlight one optimization that was particularly effective and one issue they missed or could have addressed differently.
• Ask the candidate to explain how they would approach a specific remaining issue you've identified.
• Give them 15-20 minutes to implement this additional optimization.
• Evaluate both their technical solution and their ability to explain the reasoning behind their approach.

Activity #4: Real-world Computer Vision Application

This exercise assesses a candidate's ability to apply computer vision techniques to solve a practical, domain-specific problem. It tests their creativity, problem-solving approach, and ability to translate business requirements into technical solutions.

Directions for the Company:

• Select a realistic problem relevant to your industry (e.g., product defect detection, document analysis, biometric verification).
• Provide a small dataset representative of the problem (10-20 images is sufficient).
• Define clear success criteria and constraints.
• Allow 2-3 hours for this exercise, potentially as a take-home assignment.
• Include some challenging aspects that require creative problem-solving rather than just applying standard techniques.
• Prepare questions about how the solution would evolve with more data or different conditions.
• If possible, provide access to domain experts who can answer questions about the problem context.

Directions for the Candidate:

• Develop a computer vision solution for the specified real-world problem using the provided dataset.
• Your solution should include:
• Data exploration and preprocessing
• Feature engineering or selection
• Algorithm implementation and training (if applicable)
• Evaluation of results against the success criteria
• Documentation of your approach and findings
• Balance algorithmic sophistication with practical considerations like processing time and implementation complexity.
• Identify limitations of your approach and suggest how it could be improved with additional resources or data.
• Be prepared to present your solution, explaining both technical details and business implications.

Feedback Mechanism:

• Provide feedback on both the technical implementation and the problem-solving approach.
• Highlight one particularly innovative or effective aspect of their solution and one area that could be strengthened.
• Present a variation of the problem (e.g., different lighting conditions, additional object classes) and ask how they would adapt their solution.
• Give the candidate 20-30 minutes to sketch or implement modifications to address this variation.
• Evaluate their adaptability and depth of understanding of the problem domain.

Frequently Asked Questions

How should we evaluate candidates who use different technical approaches than we expected?

Focus on the effectiveness of their solution rather than adherence to a specific approach. Different backgrounds may lead to novel solutions that are equally valid. Evaluate whether their approach solves the problem efficiently and whether they can explain the rationale behind their choices. The diversity of approaches in computer vision is a strength, not a weakness.

Should we provide access to internet resources during these exercises?

Yes, with limitations. In real-world scenarios, engineers regularly consult documentation and references. Allow access to official documentation, API references, and general resources, but discourage copying complete solutions from tutorials or forums. This approach tests their ability to find and apply information rather than memorize syntax or algorithms.

How do we fairly evaluate candidates with different specializations within computer vision?

Recognize that computer vision encompasses multiple specialties (3D vision, video analysis, deep learning, classical algorithms). Adjust your evaluation based on the candidate's background and your specific needs. Focus on transferable skills like problem decomposition, algorithm selection, and optimization principles rather than specific technique familiarity if the specialization isn't critical for the role.

What if a candidate doesn't complete the exercise within the allotted time?

Partial solutions can be highly informative. Evaluate what they prioritized, how they structured their approach, and the quality of what they did complete. Ask them to explain what they would have done with more time and why they made their prioritization decisions. This often reveals more about their problem-solving process than a rushed complete solution.

How should we balance evaluating theoretical knowledge versus practical implementation skills?

Both are important but serve different purposes. Theoretical knowledge indicates depth of understanding and potential for innovation, while implementation skills show ability to deliver working solutions. Use the system design exercise to evaluate theoretical knowledge and the implementation exercises to assess practical skills. The ideal candidate demonstrates strength in both areas, but your specific role may prioritize one over the other.

Should we customize these exercises for junior versus senior roles?

Yes, adjust the complexity and scope based on the seniority level. For junior roles, focus more on implementation of well-defined tasks and basic understanding of computer vision principles. For senior roles, emphasize system design, optimization, and handling ambiguity. The exercises can remain similar in structure but vary in complexity, constraints, and expectations.

Applied computer vision continues to transform industries through its ability to automate visual analysis tasks that once required human perception. By incorporating these work sample exercises into your hiring process, you'll be able to identify candidates who not only understand computer vision theory but can apply it effectively to solve real-world problems. The most valuable team members combine algorithmic knowledge with practical implementation skills and the ability to design scalable, maintainable systems.

Yardstick helps companies design comprehensive interview processes that accurately assess technical and soft skills for specialized roles like computer vision engineers. Our platform offers tools to create custom work samples, standardize evaluation criteria, and gather meaningful data from technical interviews. To explore more resources for hiring technical talent, visit our AI Job Descriptions generator, AI Interview Question Generator, and AI Interview Guide Generator.

Want to build a complete interview guide for evaluating applied computer vision skills? Sign up for a free Yardstick account