Overview
What this challenge is about.
Defect Detection on PCBs for a Hardware-AI Manufacturer. Advanced challenge in code. Writing production code that solves real engineering problems, earn a bl...
The Brief
What you'll do, and what you'll demonstrate.
Train a PCB defect detector that achieves recall above 90 percent at the FP budget across the 6 defect classes.
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
- Fine-tune a modern object detector on a defect-detection task
- Operate under a hard false-positive budget
- Write a model card that supports a real production decision
- Communicate model boundaries to a non-ML QA team
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Computer Vision
Master · Computer Vision
Strong alignment
This challenge maps to Computer Vision at the Master level. It sharpens the same practical skills your coursework expects — but in a real industry context with actual constraints and deliverables.
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.
- Transfer Learning
Apply transfer learning 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.
- Data Augmentation
Apply data augmentation to solve real industry problems and demonstrate production-level capability.
- Pytorch
Apply pytorch to solve real industry problems and demonstrate production-level capability.
- Ml Pipelines
Apply ml pipelines 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:
Machine Learning Engineer
Defect-detection models with a hard FP budget are the day-one MLE work at any manufacturing-AI or industrial-vision company.
This challenge sharpens
- object-detection
- transfer-learning
- ml-pipelines
Computer Vision Engineer
Defect-detection on real PCBs builds the CV-engineer judgment around class imbalance, augmentation, and per-class evaluation.
This challenge sharpens
- object-detection
- data-augmentation
- model-evaluation
AI Safety Researcher
Operating under explicit FP budgets and writing honest model cards is the safety-aware engineering pattern AI safety researchers practice.
This challenge sharpens
- model-evaluation
- object-detection
- transfer-learning