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