Skills

Jira Research Architecture

Job Specifications

Build the Bloomberg of Wine. Turn the world’s wine data into real‑time pricing & liquidity intelligence and put it in every wine professional’s hands. We’re bootstrapped, profitable, and default‑alive.

H1B sponsorship friendly

Why this role exists

Wine is a $450 billion global asset class that still runs on static spreadsheets and gut instinct. It is one of the last major markets on earth without real-time liquidity signals or centralized data.

The industry is highly fragmented, with over 1 million producers with no single player holding >1% market share, leaving it disconnected and ripe for digitization. WineLabs is the first to ingest this global chaos (merchants, auctions, exchanges, critics), structure it, and build the "Bloomberg Terminal" that powers the future of the trade.

We have strong early traction, and we need a versatile builder to help us capitalize on this massive untapped opportunity.

The mandate

You will co-own the technical architecture and AI strategy end‑to‑end with the CEO. We believe one engineer armed with the right AI stack can out-ship a traditional team of five. Your focus will be on three core intelligence loops:

LLM-based engineering: Orchestrate LLMs, agents & deterministic pipelines to ingest, structure, and sanitize the world’s messy wine data.
Generate Signals: Build the "brain" of the product by leveraging AI models to improve our data & our insights.
Close the Feedback Loop: Shipping is just the first step. You will work with early users to validate your features, identify friction points, and iterate quickly to ensure the product delivers real utility.

What you’ll do

Solve "Unstructured-to-Structured" at scale: Instead of writing brittle scrapers, you will build LLM-driven extraction pipelines. You will deploy agents capable of reading merchant catalogs in any format and converting them into strict schemas with high fidelity.
Ship GenAI Product Features: Build RAG (Retrieval-Augmented Generation) workflows to let users query the data naturally. Use LLMs to synthesize critic reviews, explain pricing volatility, or predict liquidity trends.
Own the "AI" Stack: Manage the lifecycle of our models, set up evaluation metrics, and improve our models continuously.
Founder-Level Engineering: This is not a research role. Everything you’ll ship will be used by our clients and will provide immediate value. So you’ll be constantly balancing the cutting edge of research with the pragmatism of a production system.

You’ll excel here if you have

Requirements

You are an AI-augmented builder and view LLMs as an infinite supply of interns.
Comfortable owning infra, data models, and evaluations end‑to‑end.
Evidence you have/want to build products that people care about.
Founder‑level ownership. You like closing loops and solving problems, not JIRA tickets.

Preferred Qualifications

You've built agents that browse, scrape, or perform multi-step actions.
You understand concepts like "bid/ask spread," "liquidity," or "order books" and want to apply them to a physical asset.
You don’t need to be a wine expert, but you’re curious why a bottle of Burgundy costs $15,000 and want to map the data behind it.