Computer Vision Engineer for Sports Card Recognition

We are seeking a US-based computer vision and full stack developer to build a platform for sports card recognition. The project includes developing subscription management, dashboards, and user account features. The ideal candidate will have experience creating scalable applications and integrating computer vision capabilities into a user-friendly platform. Hiring: Computer Vision + Full Stack Developer for Sports Card Live Auction Overlay App (SaaS) 📌 Overview I’ve built an MVP of a real-time sports trading card scanning and comping overlay tool using Loveable.dev. The product helps buyers gain an edge during live auctions by instantly identifying cards and showing real-time market comps. Now I’m looking for a U.S.-based developer (or strong US-aligned freelancer) to take this from MVP → production SaaS. This is a subscription-based product, so I need someone who can help build something fast, accurate, scalable, and hard to replicate. 🧠 What the product does Users can: Capture or upload sports trading card images during live auctions (mobile + desktop) Instantly identify: Player Year / set Parallel / serial number Pull live market comps Display a real-time “buy / avoid / fair price” overlay The goal is speed + accuracy in live buying situations (seconds matter). ⚙️ What I already have MVP built in Loveable.dev Basic overlay + UI flow Initial comp logic concept Subscription idea (not yet fully implemented) 🛠️ What I need help building (Phase 1 → Scale) I’m looking for someone to help rebuild and harden the system into a real SaaS product: 1. Computer Vision / OCR Layer Card detection from images (mobile + desktop) OCR extraction (player name, set, serial numbers) Image recognition / matching to known cards Confidence scoring (very important — must avoid wrong matches) 2. Comp Engine (Core Value) Integrate or build system for: eBay sold listings 130point or similar comp sources Card Ladder / ALT-style pricing logic Return: last sale average comp trend direction liquidity estimate 3. Real-Time Overlay System Lightweight overlay that works during live auctions Low latency (fast lookup is critical) Works on mobile + desktop workflows 4. SaaS Infrastructure User accounts + authentication Subscription billing (Stripe) Usage tracking / rate limiting Admin dashboard 5. Scaling / Production Hardening API architecture improvements Database structure Performance optimization for real-time use Error handling for imperfect images 💡 Ideal candidate You should have experience with: Computer vision (OpenCV, YOLO, or similar) OCR pipelines AI image classification or similarity matching Full-stack SaaS development Stripe subscriptions API design (Node.js / Python / Next.js preferred) Huge plus if you have: Sports card / collectibles knowledge Experience with marketplaces or scraping pricing data Real-time / low-latency systems 🎯 Why this is interesting This is not a generic app. It’s: A real-time decision engine for high-value collectibles Built for a passionate, high-spend niche (sports cards) Subscription-based with strong monetization potential Designed for speed advantage in live auctions 📍 Requirements Must be U.S.-based (preferred for communication/time zone alignment) Must be able to work independently Must have strong GitHub/code examples Bonus if you’ve built AI or vision-based SaaS tools before 💰 Budget Open to: Hourly or fixed project 📩 To apply, please include: Relevant CV / GitHub Past AI / computer vision projects Any SaaS or startup experience Your approach to building a real-time image → comp system Availability per week