Product-Minded Software Engineer
Summary
Build and scale backend systems to automate restaurant procurement, replacing manual workflows with AI-driven software for pricing, orders, and matching.
You’ll contribute to the technical backbone that turns a traditionally ops-heavy GPO business into a scalable software platform. The goal is to make it possible for independent restaurants to buy with the power of a chain, while reducing reliance on manual operations.
The problem you'll own: Replace manual, people-dependent procurement workflows with trustworthy software that is accurate, scalable, and self-serve
The systems you'll build: Contribute to the technical platform, including tech architecture, order workflows, matching and pricing logic, data foundations, and automation layers that support operations and user experience
The change you’ll drive: Turn messy food procurement processes into reliable product workflows
What success looks like: The business scales without losing pricing accuracy, order integrity, or operational trust, and the team can serve more restaurants with less manual work
Must have
Backend depth - built and scaled production systems, with proven ability to architect and orchestrate multi-service applications
Stack fluency - deep hands-on expertise in TypeScript/Node.js and Python, plus React, Flutter or similar on the frontend
Data modeling - modeled messy real-world data into trustworthy systems at scale
Think in AI automation - track record of automating operations-heavy processes with AI, avid use of state-of-the-art AI development tools as Claude Code
Product judgment - led E2E architecture through launch with a bias for speed and impact; made pragmatic tradeoffs to solve user problems
Nice to have
Domain - built B2B platforms such as procurement, marketplaces, or supply chain, ideally in a startup or scale-up environment
Money movement - worked on billing, invoicing, payments, or reconciliation systems
Team leadership - scaled and led technical teams through architecture, code review, and engineering standards
Competitive salary and meaningful equity
Real AI work in production, not prototypes
A business past product-market fit and into scaling
Low-ego, high-ownership team culture
Direct access to founder and leadership team