Overview
Build and scale backend systems to automate restaurant procurement, replacing manual workflows with AI-driven software for pricing, orders, and matching.
Full job description
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
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Verification notes
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