Build a Face-Anonymization Tool for a Civic-Tech Newsroom
Overview
What this challenge is about.
Build a Face-Anonymization Tool for a Civic-Tech Newsroom. Intermediate challenge in code. Writing production code that solves real engineering problems, ear...
The Brief
What you'll do, and what you'll demonstrate.
Ship a reviewer-in-the-loop face-anonymization tool that cuts hand-blurring time by 75 percent without missing high-risk faces.
This is not a coding exercise. It is the work a software engineer does between a Jira ticket and a merged PR. That distinction matters to every hiring manager who has seen candidates solve LeetCode problems and none who have shipped production code under real constraints.
When you finish, you will have something most graduates do not: a real-world deliverable, verified by Ewance, that you can show to a hiring manager and say "I did this. Here is the proof."
Earning criteria — what you'll demonstrate
- Integrate a pretrained face detector into a working tool
- Build a reviewer-in-the-loop UI that respects journalist workflow
- Evaluate detection with precision/recall on a representative test set
- Document a privacy-sensitive tool for non-technical users
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Object Detection
Apply object detection to solve real industry problems and demonstrate production-level capability.
- Image Processing
Apply image processing to solve real industry problems and demonstrate production-level capability.
- Opencv
Apply opencv to solve real industry problems and demonstrate production-level capability.
- Model Evaluation
Apply model evaluation to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
- Ui Prototyping
Apply ui prototyping to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
AI Engineer
Wrapping a pretrained CV model in a reviewer-friendly tool is the day-one AI-engineering work at any media, legal, or compliance-AI company.
This challenge sharpens
- object-detection
- image-processing
- ui-prototyping
Computer Vision Engineer
Evaluating detection precision/recall on a domain-specific set is the CV-engineer skill that distinguishes shippable from demo.
This challenge sharpens
- object-detection
- model-evaluation
- opencv
AI Product Designer
Designing a reviewer UI that respects journalist workflow is exactly the AI product designer's craft of fitting AI into a human process.
This challenge sharpens
- ui-prototyping
- image-processing
- object-detection