Overview
What this challenge is about.
Build a Vision-Language Search for an E-commerce Catalog. Advanced challenge in code. Writing production code that solves real engineering problems, earn a b...
The Brief
What you'll do, and what you'll demonstrate.
Ship a vision-language search prototype over a 600k-SKU catalog that beats the keyword baseline on a 100-query human eval.
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
- Apply vision-language models to a real retrieval problem
- Design honest human-graded retrieval evaluations
- Combine semantic and structured filters in production retrieval
- Quantify uplift over a keyword baseline that already works
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.
- Vision Language Models
Apply vision language models to solve real industry problems and demonstrate production-level capability.
- Clip
Apply clip to solve real industry problems and demonstrate production-level capability.
- Vector Search
Apply vector search to solve real industry problems and demonstrate production-level capability.
- Retrieval Evaluation
Apply retrieval evaluation to solve real industry problems and demonstrate production-level capability.
- Pytorch
Apply pytorch to solve real industry problems and demonstrate production-level capability.
- Qdrant
Apply qdrant 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
Shipping vision-language retrieval at catalog scale with honest evaluation is the work MLEs do on search and discovery teams at retail and marketplace companies.
This challenge sharpens
- vision-language-models
- vector-search
- retrieval-evaluation
Computer Vision Engineer
Working with CLIP-class encoders in production and tuning retrieval over real images is exactly the work CV engineers do on AI-first product teams.
This challenge sharpens
- vision-language-models
- clip
- pytorch
AI Engineer
Building the end-to-end vector-search service plus eval harness is core AI-engineer work at companies adopting semantic search.
This challenge sharpens
- vector-search
- qdrant
- vision-language-models